Talk2Nature

A LIVING ATLAS / PLACES, PATTERNS, PEOPLE

A world of signals.
A history of listening.

From a honeybee’s dance to a whale’s coda. Explore where earlier work happened, what was observed, and what we can learn from it.

108 library notes · 41 with cited locations · 67 without a pin
This is our growing literature collection, not every study worldwide or a map of individual observations. Local app recordings and private study records are not published here.

FOLLOW A PLACE. FIND A QUESTION.

The world is already talking.

44 approximate regions
World land silhouette; study regions are described in the timeline below.

Numbers count library notes, not animals or calls. Nearby pins group together on smaller screens.

Study regions and animal origins, shown approximately. Map: Natural Earth, public domain ↗

Every entry is available in the timeline below. Interactive pins and filters require JavaScript.

ACROSS TIME / FOLLOW THE EVIDENCE

One question leads to another.

108 evidence notes, oldest source years first.

The timeline uses publication or documentation years; ranges are sorted by their latest year. Observation dates are shown separately when checked. An empty region is a gap in this collection, not evidence that nobody has studied it.

1973Lecture year

Insects / Historical primary account

Before AI: learning from the honeybee dance ↗

Von Frisch’s lecture connects waggle-dance duration and orientation with food distance and direction. It also recounts a 1949 hive-transfer experiment probing orientation with unfamiliar surroundings.

Wolfgangsee, Austria

1949 orientation trial described in a 1973 lecture

Evidence, location & limits

This is a retrospective account of bounded honeybee signaling and navigation. It does not establish a general conversation system or justify transferring the same code to other animals.

Selected lecture passages on dance distance/direction and the 1949 orientation trial reviewed; PDF pages 9–10 visually checked. The referenced original experiments and subsequent literature have not all been reviewed.

The lecture names Brunnwinkl on Wolfgangsee for a 1949 hive-transfer trial. The pin represents the lake region, not all experiments in the lecture.

  • Wolfgangsee, Austria · Study region

    The lecture names Brunnwinkl on Wolfgangsee for a 1949 hive-transfer trial. The pin represents the lake region, not all experiments in the lecture.

    Location source ↗ · Lecture PDF pages 9–10, printed pages 84–85

Observation-date source ↗ · Lecture PDF page 9, final paragraph

Location metadata reviewed: 2026-10-03. Map points are approximate locators, not sample coordinates.

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1980Original study year

Mammals / Alarm-call context and receiver-response studies

Context helps vervets interpret alarm calls ↗

The 1980 publisher summaries report three Amboseli groups observed over 14 months and predator-absent alarm-call playbacks; the longer companion abstract says call length, amplitude and caller age/sex had little effect on response quality, while context was not a systematic determinant in that experiment. The 1980 Science abstract reports different receiver behaviors to predator-call playbacks. Fischer (2020) separates comparatively constrained call production from learned receiver responses. In a later South African field study, eight adult female subjects from one group completed 16 within-subject playback trials using two alarm-bark exemplars from one male. Subjects looked toward the speaker for less time and showed lower vigilance during an ongoing intergroup encounter than when no encounter was underway; the authors reported no predator-specific escape responses. This is evidence that surrounding context can change measured responses to a call in this bounded setup.

Amboseli region, Kenya · KwaZulu-Natal region, South Africa

Observation dates not established here

Evidence, location & limits

The full 1980 primary articles and their detailed methods were not obtained. The 2023 result is small and local: eight adult females, one group, 16 trials and two exemplars from one male; no predator-specific escape responses occurred. Its Results section and Discussion report opposite directions for the startle outcome, so that measure is unresolved here. The OSF API confirms public project metadata only; the project-level license relationship is null, while dataset/code versions, file contents, component-specific rights, privacy conditions and wildlife-site identifiers remain unknown. The publisher-provided view-only URL's HTTP 403 is route-specific, not proof of global OSF inaccessibility. Fischer (2020) is a secondary synthesis, not a reanalysis. Together these studies do not establish a word-like call dictionary, context-free meaning or general animal translation. Context-specific calling and receiver response must be measured separately, across sufficiently broad contexts and with independent outcomes.

Wiley's 1980 publisher abstract and German summary, the ScienceDirect publisher abstract for the 1980b companion paper, the 1980a Science PubMed abstract, selected full-text sections of Fischer (2020, pp. 108–114), and the full open-access 2023 Springer primary article were reviewed. The 2023 paper's methods, measured responses, sample scope, rights statement and data-availability statement were checked. On 2026-10-25, an unauthenticated GET to the canonical OSF API v2 node endpoint succeeded: it reports the linked project as public, gives its title and creation/modification dates, and returns a null project-level license relationship. The publisher-provided view-only URL separately returned HTTP 403. No OSF file listing, contents or component-level terms were reviewed.

The historical study places observed groups in Amboseli, Kenya; the 2023 study names a field population in KwaZulu-Natal, South Africa. The map uses coarse regional locators only; the 2023 paper's precise wildlife-site coordinates are deliberately not reproduced.

  • Amboseli region, Kenya · Study region

    The original summary places three observed groups in Amboseli, Kenya. The region pin does not identify a troop or precise field site.

    Location source ↗ · 1980 publisher abstract and German summary (Zusammenfassung)
  • KwaZulu-Natal region, South Africa · Study region

    The 2023 article identifies its field population in KwaZulu-Natal, South Africa. Only an approximate province-level locator is mapped; exact coordinates printed in the paper are omitted.

    Location source ↗ · Methods: Study site and groups

Location metadata reviewed: 2026-10-25. Map points are approximate locators, not sample coordinates.

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1985Original study year

Marine mammals / Historical acoustic study

A shared song can change across years ↗

Across 163 songs sampled near Bermuda, songs within a year were similar while their content changed across years. The series covers 13 sampled years between 1957 and 1975.

Waters near Bermuda

April–May in 13 sampled years within 1957–1975

Evidence, location & limits

Seasonal sampling is not a continuous 19-year recording. Changing structure does not reveal translated meaning; the later review discusses unresolved functions of song in the mating system.

Indexed 1985 publisher abstract and 2017 review abstract; full methods, spectrograms and original audio not reviewed.

The original abstract explicitly locates the recordings near Bermuda. One broad waters-region marker represents the collection.

  • Waters near Bermuda · Study region

    The original abstract explicitly locates the recordings near Bermuda. One broad waters-region marker represents the collection.

    Location source ↗ · 1985 publisher abstract, opening recording and comparison description

Observation-date source ↗ · 1985 publisher abstract, first sentence

Location metadata reviewed: 2026-10-04. Map points are approximate locators, not sample coordinates.

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2006Source year

Fish / Field observation and manipulation

Two hunters, different skills, a visible signal ↗

Groupers made visual movements before coordinated hunting with moray eels. The study links signaling to hunting context and reports benefits from complementary hunting behavior.

Ras Mohammed region, Egypt

Observations: September 2002–December 2004

Evidence, location & limits

Individual identities were not always verifiable, and successful hunts were too sparse for the main success analysis to treat each individual separately. This is bounded cooperation, not a general language.

Selected results, discussion and Materials and Methods reviewed; videos and raw records not reviewed.

The methods locate observations in Mersa Bareika, Ras Mohammed National Park, Egypt. The pin represents the broader region.

  • Ras Mohammed region, Egypt · Study region

    The methods locate observations in Mersa Bareika, Ras Mohammed National Park, Egypt. The pin represents the broader region.

    Location source ↗ · Materials and Methods: Study site and subjects

Observation-date source ↗ · Materials and Methods: Study site and subjects

Location metadata reviewed: 2026-10-04. Map points are approximate locators, not sample coordinates.

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2011Source year

Birds / Observational gesture study

Sometimes the signal is something shown ↗

Researchers observed ravens showing or offering non-food objects to other ravens, followed by attention to the object and social interaction.

Northern Alps, Austria

Observation dates not established here

Evidence, location & limits

The abstract reports 38 interactions across seven pairs. The proposed role in testing or strengthening relationships is an interpretation, not proof of human-like declarative intent.

Publisher abstract and figure captions reviewed; full methods and supplementary videos not obtained.

The abstract places observations in the Northern Alps, Austria. Three field seasons are mentioned, but calendar years were not established.

  • Northern Alps, Austria · Study region

    The abstract places observations in the Northern Alps, Austria. Three field seasons are mentioned, but calendar years were not established.

    Location source ↗ · Publisher abstract, observations paragraph

Location metadata reviewed: 2026-10-04. Map points are approximate locators, not sample coordinates.

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2012Publication year

Orange-fronted conure (Eupsittula canicularis; the 2012 paper uses Aratinga canicularis) / Controlled playback study

A parrot can imitate a call to address another bird. What the experiment shows. ↗

Balsby, Momberg and Dabelsteen tested whether a call resembling one of two paired birds would selectively affect that bird. They studied 36 temporarily captive wild conures from 27 flocks. After exclusions, 44 of 72 trial periods were analyzed. Targeted birds responded with higher call rates and shorter latency overall, while some non-target responses were also recorded. The authors interpret this as evidence that vocal imitation can address a specific individual in this species and setting.

Costa Rica

Playback experiments: June–August 2007

Evidence, location & limits

This was a controlled playback study of 36 wild-caught orange-fronted conures from 27 flocks, paired in short-term outdoor aviaries during 2007. Of 72 trial periods, 44 were retained for analysis after caller-assignment and outside-interaction exclusions. The authors report higher call rates and shorter response latency for the targeted bird overall, with a sex-specific call-rate pattern; non-target birds also responded. This supports a bounded, context-specific receiver response to acoustically targeted imitation, not decoding of sentence meaning, general conversation, or a result transferable to pet parrots, other species or phone speakers. The experiment used captured birds, repeated playback and video observation under research permits. No Talk2Nature replication or independent specialist review exists.

Reviewed the full primary PLOS article text for the abstract, introduction, Materials and Methods, Results, Discussion, Ethics Statement and article license. No raw recordings, associated dataset, supplementary material or analysis code were acquired or inspected; no reanalysis was performed.

The methods place the experiment in Santa Rosa National Park, Guanacaste, Costa Rica. The public map uses the existing country-scale point and omits exact site coordinates.

  • Costa Rica · Study region

    The primary article names the Costa Rica research site. The atlas links the study to the existing country-scale point; no exact coordinates are published.

    Location source ↗ · Materials and Methods, study site and dates

Observation-date source ↗ · Materials and Methods, study dates

Location metadata reviewed: 2026-10-12. Map points are approximate locators, not sample coordinates.

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2013Source year

Marine mammals / Controlled playback study

A familiar whistle can address an individual ↗

Dolphins responded vocally to copies of their own signature whistles more than to the control whistles described in the study, supporting individual-directed use of learned signals.

Eastern Scottish waters, UK

Observation dates not established here

Evidence, location & limits

Individual addressing does not establish open-ended conversation. The authors discuss alternative mechanisms; this note has not independently checked the complete experimental design.

Indexed primary abstract, discussion and methods location passage reviewed. Complete methods, supplements and raw recordings not obtained.

The methods passage places group follows off eastern Scotland, including the Moray Firth. One broad regional point is used; it is not a sample coordinate or the authors’ institutional address.

  • Eastern Scottish waters, UK · Study region

    The methods passage places group follows off eastern Scotland, including the Moray Firth. One broad regional point is used; it is not a sample coordinate or the authors’ institutional address.

    Location source ↗ · Indexed primary Materials and Methods opening location passage

Location metadata reviewed: 2026-10-04. Map points are approximate locators, not sample coordinates.

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2013Source year

Mammals / Captive social-context experiment

Who leaves matters to the wolves who stay ↗

In two captive wolf packs, howling after separation was associated with social relationships. The measured cortisol difference did not explain howling frequency within the test condition.

Austria · captive wolf study

Observation dates not established here

Evidence, location & limits

Nine captive, human-raised wolves do not represent every wild pack. A null cortisol association does not establish absence of emotion or a decoded message.

Selected results, discussion and main methods via Europe PMC XML; supplementary procedures, data and statistical replication not reviewed.

The study identifies the Wolf Science Center, Austria. A country-level locator is used; no exact enclosure or observation date is inferred.

  • Austria · captive wolf study · Study region

    The study identifies the Wolf Science Center, Austria. A country-level locator is used; no exact enclosure or observation date is inferred.

    Location source ↗ · Results: opening paragraph naming the study population

Location metadata reviewed: 2026-10-04. Map points are approximate locators, not sample coordinates.

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2016Source year

Bats / Observational study

Bat calls: context comes from observation ↗

This study paired bat social calls with synchronized video to label caller, recipient, circumstances and subsequent behavior.

Animal origin · Herzliya region, Israel

Observation dates not established here

Evidence, location & limits

The main analysis used 14,863 calls from seven adult females. Many recordings were excluded when identification was uncertain. Captive social calls and specialized ultrasonic recording do not establish phone or wild-population performance.

Primary Materials and Methods sections on housing, monitoring and classification tasks via PMC. Full statistics, supplementary analyses and raw data not reviewed.

The methods identify the bats’ capture origin near Herzliya. This pin is the animal-origin region, not the location of the acoustic chambers.

  • Herzliya region, Israel · Animal origin, not recording site

    The methods identify the bats’ capture origin near Herzliya. This pin is the animal-origin region, not the location of the acoustic chambers.

    Location source ↗ · Materials and Methods: Animal retrieval and care

Location metadata reviewed: 2026-10-03. Map points are approximate locators, not sample coordinates.

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2016Source year

Birds / Behavioral experiment

Japanese-tit call order: evidence and interpretations ↗

Japanese great tits showed different receiver behaviors under component calls, natural versus reversed order, and single- versus two-source playback; a bounded novel cross-species sequence also elicited stronger responses than its reversal, while a matched ABC–zi control was weak. In the 2022 deposited binary table, the natural single-source 1A-R condition had an approach event in 13/16 flock trials and wing-flicking in 15/16; the other three treatments had approach events in 2–3/16 and wing-flicking in 1–4/16. These aggregate counts reproduce the descriptive pattern only. They do not establish a unique mechanism: one published critique agrees that receiver behavior is sensitive to order and source but disputes that this demonstrates Core-Merge or hierarchical syntax.

Karuizawa region, Japan

Observation dates not established here

Evidence, location & limits

These are species-, population- and task-bounded receiver-response results, not general animal-language or translation evidence. The 2017 novelty test combined a familiar heterospecific call type with Japanese-tit calls; it does not establish open-ended productivity, and its comparison with ABC–D used earlier data from another year. The 2022 study extends the same research line in the same species, not an independent replication. Its two Figshare v1 outcome tables contain no audio and no location fields; dates and caller_ID fields remain in ignored local files. The current official Figshare API reports MD5 values and the two received data tables match them; this confirms byte agreement with the current provider manifest, not scientific validity or a data-to-figure reproduction. The committed summary contains aggregates only. Its script does not run the deposited R code or reproduce mixed-effects models and adjusted contrasts. Schlenker et al. (2024) offer two formal accounts that fit the source-separation result under different assumptions. Beckers et al. (2024) agree the evidence shows sensitivity to call order and distinguishable responses to 1AR versus 2AR, but argue that the experiment does not establish Core-Merge or hierarchical syntax; a finite-list account fits the 2022 result, and a finite-state/concatenation account is proposed for the broader order pattern. This is a published interpretive disagreement, not a new empirical result or consensus. The 2025 Carolina-wren/great-tit study is adjacent cross-family receiver-response evidence; it did not reverse order and is not a test or replication of Japanese-tit sequence-order sensitivity. No Talk2Nature playback or animal-facing experiment is proposed.

Direct publisher full text reviewed for the 2016 primary paper (Methods, Experiments 1–2, Results, figures and ethics) and 2022 follow-up (selected Methods, Results, ethics, data/code statements and rights). The linked 2022 Figshare v1 tables were checked against the current official API sizes and MD5 values, paired on trial metadata, and summarized with the project standard-library script; the deposited R scripts were read as text, not executed. For the 2017 paper, publisher-indexed primary text exposed the abstract, selected Results, STAR Methods, statistics and data statement; its direct ScienceDirect page returned HTTP 403. The 2017 Mendeley Data v1 landing metadata and record-level license were reviewed, but its files were not. For Schlenker et al. (2024), the main text (Sections I–VII) of the official HAL v1 author-accepted manuscript was reviewed; the formal appendices were inspected selectively, not recalculated. The separate Wiley supporting-information PDF returned HTTP 403 and was not reviewed. Beckers et al. (2024) Sections 1–4 were read in the full text available through PubMed Central; its conceptual critique is not a new animal experiment. For the adjacent Coye & Dutour (2025) study (source 268), publisher-indexed Methods, Results and Discussion passages and the University of Western Australia repository abstract/access record were reviewed; direct ScienceDirect retrieval returned HTTP 403, so the full article file, figures, supplement and complete statistical details were not reviewed. No inferential model was reproduced and no independent Talk2Nature specialist review has occurred.

The methods name forests near Karuizawa. The displayed region does not identify nest boxes.

  • Karuizawa region, Japan · Study region

    The methods name forests near Karuizawa. The displayed region does not identify nest boxes.

    Location source ↗ · Methods: study site and call recordings

Location metadata reviewed: 2026-10-03. Map points are approximate locators, not sample coordinates.

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2016Published online 2015; issue

Birds / Operant playback study

Playback can shape the call you measure ↗

Five individually housed budgerigars had been trained to call after a visual LED cue, with food reinforcement. In a call-plus-flock condition, calls were louder than in silent, call-only, flock-only, and pure-tone conditions. Among the three birds that produced non-primary calls, these calls were more likely in the call-plus-flock and call-only conditions than in silent, flock-only, and tone conditions. The result shows acoustic context can modulate a trained vocal response in this laboratory setup; it does not decode a message.

Location awaiting review

Observation dates not established here

Evidence, location & limits

The study involved five birds (one male, four females), not a household population or a held-out-individual test. The animals were trained in an operant setup and the experimental conditions followed a fixed sequence; this was not passive observation of spontaneous calls. The call-variation result applied to three birds. Vocal intensity and call form are not measures of comprehension, welfare benefit, or semantic meaning, and no receiver-response test was reported. The paper describes maintaining birds at 85–90% of free-feeding weight during the experiment and reports institutional animal-care approval; this protocol should not be copied as consumer-app guidance. The PMC record lists supplementary material, but it was not inspected. Raw data/code and their rights were not established in this review.

PubMed citation/abstract and selected PMC author-manuscript sections surfaced by the article index—subjects, apparatus, stimuli, procedures, analyses, and results/discussion—reviewed 2026-10-14. The listed supplement, raw data, and code were not inspected; no reanalysis, replication, or independent review. Reuse terms were not verified.

Location has not yet been checked in primary sources.

Location metadata reviewed: Not yet reviewed. Map points are approximate locators, not sample coordinates.

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2016Source year

Birds / Human–wildlife cooperation study

A shared task can make a shared signal useful ↗

Researchers found that a specialized honey-hunting call used by people in Mozambique increased cooperative guiding by wild honeyguides compared with other human or animal sounds.

Niassa Reserve region, Mozambique

Observation dates not established here

Evidence, location & limits

The finding concerns a specific cooperative tradition and task. It is not evidence of unrestricted conversation. Local knowledge and the people who maintain this relationship are part of the research context.

Primary abstract and indexed passages from the university’s research-team account reviewed. Full paper methods, supplementary material and data were not obtained; no independent replication performed.

Region verified in the institutional research account; detailed experimental methods and observation dates remain unreviewed.

  • Niassa Reserve region, Mozambique · Study region

    The university’s research-team account identifies experiments with honey-hunters and honeyguides in Niassa National Reserve, Mozambique.

    Location source ↗ · Opening study description: Niassa National Reserve

Location metadata reviewed: 2026-10-04. Map points are approximate locators, not sample coordinates.

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2016Publication year

Lilac-crowned Amazon (Amazona finschi) / Field bioacoustic context study

Lilac-crowned Amazon calls vary with context; meaning remains a question ↗

Montes-Medina, Salinas-Melgoza and Renton analyzed 75 hours of non-continuous field recordings from 61 free-living Lilac-crowned Amazons. They report 8,622 notes classified into 152 note-types; after excluding single-occurrence types, 8,571 notes formed 101 note-types emitted at least twice. The researchers associated calls with nine behavioral contexts, and common note-types occurred in several contexts with different proportions. Context was associated with acoustic features and note-type frequency. Separately, a nested permuted discriminant analysis distinguished 28 note-types while accounting for repeated notes from individuals (46.9% observed classification versus 9.6% expected under the paper's permutation null, P=0.001). That is note-type discrimination, not prediction of behavior from sound and not evidence that calls have human-language meanings.

Chamela–Cuixmala region, Jalisco, Mexico

Observation dates not established here

Evidence, location & limits

This is a single-species field study with non-continuous recordings and researcher-assigned behavioral contexts, not a validated context classifier or modern ML benchmark. The article notes low sample sizes in some contexts and says playback evidence is needed to establish signal function. GLMM analyses excluded food soliciting; the pDFA classifies note types, not behavioral contexts, and its permutation comparison is not an untouched held-out-animal test. Some off-nest individual identities were inferred from day and distance. The paper does not report quantified independent context-label agreement. Supplement descriptions conflict with several linked file extensions; no data or example audio was opened, and the listed files do not describe a full 75-hour archive. The acoustic setup and wild species do not validate ordinary phone capture or household-parrot behavior.

Reviewed the full publisher article for abstract, field methods, context definitions, acoustic measurements, statistical analyses, results, discussion, data availability, ethics and rights; reviewed the additional-file inventory. The research viewer failed or timed out on selected binary supplements. No article assets, audio, data or code were downloaded, opened, executed or redistributed; no independent reanalysis or specialist review was performed.

The paper names the Chamela–Cuixmala Biosphere Reserve on the Jalisco coast and gives study-region bounds. This coarse marker represents the study region only, not nest sites or animal locations.

  • Chamela–Cuixmala region, Jalisco, Mexico · Study region

    The full methods name the reserve and provide the region's coordinate bounds; the public point is deliberately rounded to a broad regional centroid.

    Location source ↗ · Methods: study area and Vocal recording

Location metadata reviewed: 2026-10-13. Map points are approximate locators, not sample coordinates.

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2017Publication year

Insects / Computer-vision signal-decoding study

A camera can decode a known bee signal—with calibration ↗

Wario et al. built a pipeline to detect honeybee waggle runs from a one-frame observation hive, estimate run orientation and duration, and map dances back to the field. The experiment used a small colony of 2,000 Apis mellifera carnica and one modified high-speed camera on each side of the comb, with infrared illumination. The paper specifies about 100 frames per second and at least 1.5 pixels per millimeter. Its temporal CNN filtered candidate image sequences; 8,239 manually labeled detections from two days were used, with 20% reserved for validation. The authors report 90.07% validation accuracy, corresponding to 89.8% recall at 95% precision. Orientation was compared with eight human tracers on 200 waggle runs, with mean error −2.92° and SD 7.37°. In a five-hour illustration, 571 detected dances from bees trained to a feeder 342 m southwest of the hive clustered around the feeder direction. This decodes a predefined, previously studied signal against a known physical outcome; the algorithm does not discover what arbitrary animal movements mean.

Location awaiting review

Observation dates not established here

Evidence, location & limits

The 90.07% figure is validation performance, not an independent colony-, site-, camera- or season-held-out result in the reviewed account. The work concerns one species, a specialized observation hive, controlled illumination and a known feeder; translating waggle duration to distance required empirical calibration with known locations. The map aggregates dances and their run measurements; it is not a natural-language exchange with a bee. The author-linked repository is pinned to commit 3aaa5099aac698bd08df1d55f4cd5183d32f7e7d, but GitHub reports no recognized license and its inspected tree has no LICENSE/COPYING file, so the code, compiled module and ground-truth data remain metadata-only. A separate 2015 BeesBook system paper describes a more elaborate tracking setup; its image-analysis pipeline took about 500 times real time on one processor for that paper's long-term dataset, while its supercomputer run used about 1,000 processors. That figure is not a runtime measurement of the 2017 waggle-run detector, but it illustrates why camera claims must be tied to the exact task and hardware.

Targeted PLOS ONE full-text sections reviewed: abstract, communication background, recording setup, target features, software modules, validation/results, discussion and license. The official GitHub README, API metadata and complete recursive tree were reviewed at pinned commit 3aaa5099aac698bd08df1d55f4cd5183d32f7e7d. Selected setup, validation, runtime and rights sections of the related 2015 Frontiers system paper were reviewed. No data, video, code, weights or supporting files were downloaded or executed; no reproduction or independent review.

Location has not yet been checked in primary sources.

Location metadata reviewed: Not yet reviewed. Map points are approximate locators, not sample coordinates.

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2006–2017Source year

Scientific method / Claim & critique

Ice-crystal claims: why beautiful images need stronger tests ↗

The 2006 pilot reports that 100 blind judges gave higher beauty ratings to 24 selected treated-condition images than to 16 control images (means 2.87 vs 1.88; one-tailed p=.001). The images came from just four bottles (two per condition), and only drops with a discernible crystal were photographed. The same investigator group’s 2008 triple-blind follow-up used six bottles (two per condition) and 300 drop photographs. Its authors reported a weak three-condition beauty effect (p=.03) and a treated-versus-proximal-control comparison at p=.05, one-tailed; the beauty effect was not significant after adjustment for image interest (p=.20), and the three-condition objective-contrast result was not significant (p=.25). These are reports by the study authors, not independent confirmation.

No single mapped site

Observation dates not established here

Evidence, location & limits

The 2006 image-level ratings do not create more independent treated water sources than the four bottles; the authors themselves noted different treatment/control environments, selective photographing of visible crystals and the need to assess all drops and use more objective measures. The 2008 authors modeled bottles as random effects but used only two bottles per condition. Stokes’s 2009 letter specifically critiques the 2008 treated-versus-proximal comparison: treating the four bottles as the units gives a maximum one-tailed exact rank probability of 1/6 (.167). This letter does not directly analyze the 2006 pilot. The 2017 technical note discusses dependent photographs and too few bottles; its authors also acknowledge their own judge-level tests were invalid and their two-bottle-per-condition trials too small. That small methods study is not a decisive independent null replication. No reviewed study shows that water understands words, carries a message, or reliably changes crystal structure because of thought.

Reviewed the full 2006 four-page article, full 2008 Journal of Scientific Exploration paper, Douglas Stokes’s 2009 letter (p. 83), and Kokubo, Nemoto and Kawano’s 2017 technical note (pp. 73–86). Checked the reported designs, outcomes and experimental units. The 2006 full text was read from an author-hosted copy; its indexed PubMed record remains linked. Raw photographs, original analysis files/code and a full systematic literature search were not reviewed; no independent specialist review was obtained.

This note covers a tool, benchmark, method or claim review rather than one verified observation site.

Location metadata reviewed: 2026-10-03. Map points are approximate locators, not sample coordinates.

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2019Source year

Fish / Controlled field experiment

Fish hear a habitat, not a translated sentence ↗

A field experiment compared reef-sound playback with dummy-speaker and no-speaker controls. Juvenile fish recruitment and retention increased on acoustically enriched experimental reefs.

Lizard Island region, Australia

Study period: October–December 2017

Evidence, location & limits

This tests responses to a soundscape in one setting and season. It does not identify the meaning of particular fish calls, establish lasting reef recovery or support unsupervised playback by app users.

Abstract, selected results/discussion and Methods passages on study site, acoustic treatment and controls reviewed. Full supplements, analysis code and independent replication not reviewed.

Experimental reefs are generalized to the island region; no individual reef positions are reproduced.

  • Lizard Island region, Australia · Study region

    The methods locate the work in the lagoon southwest of Lizard Island Research Station, on the northern Great Barrier Reef. The pin represents the wider island region.

    Location source ↗ · Methods: Study site

Observation-date source ↗ · Methods: Study site

Location metadata reviewed: 2026-10-04. Map points are approximate locators, not sample coordinates.

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2020Source year

Cats / Dataset

Cats: useful labels, limited reuse ↗

CatMeows documents 440 sounds from 21 cats with context, cat, owner and session identifiers.

Location awaiting review

Observation dates not established here

Evidence, location & limits

CC BY-NC 4.0 is not general commercial permission. Elicited contexts should not become app instructions; files were not acquired.

Version 1.0.2 record, API rights and naming conventions; paper abstract only.

Location has not yet been checked in primary sources.

Location metadata reviewed: Not yet reviewed. Map points are approximate locators, not sample coordinates.

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2021Scientific Reports, December 2021

Rhesus macaque, bottlenose dolphin and Egyptian fruit bat / Machine-learning method

BioCPPNet separates overlapping calls—it does not decode them ↗

BioCPPNet is a supervised source-separation pipeline, an audio-cleanup step rather than a communication decoder. The study constructs mixtures by additively combining known individual vocalizations, then trains a lightweight U-Net to estimate separated waveforms. It evaluates macaque, dolphin and Egyptian-fruit-bat material. For bats, the paper reports 31,399 calls from 15 highly represented individuals, with 12 used in a closed-speaker regime and three held for an open-speaker evaluation. It reports bat validation SI-SDR 9.7 and downstream caller-identity accuracy 57.5% for the closed-speaker setting; the open-speaker SI-SDR is reported as 10.0. Those results concern reconstruction and identity on generated mixtures, not behavior, context meaning, receiver comprehension or conversation.

Location awaiting review

Observation dates not established here

Evidence, location & limits

The mixtures are constructed from known isolated source recordings, so the reported experiment does not by itself validate performance on naturally recorded, uncontrolled field mixtures. SI-SDR and caller-identity accuracy are reconstruction proxies, not evidence that a model identified call purpose or meaning. The bat open-speaker test uses three held-out individuals from the selected subset, and Talk2Nature has not reproduced the pipeline or audited source/session overlap. The repository has no visible LICENSE at the reviewed commit, so implementation reuse is not cleared. Dataset, source audio, generated mixtures, model weights and paper terms remain separate; dolphin data are explicitly restricted. No independent reproduction or expert review exists.

Reviewed the article abstract, Methods, Data availability, Code availability, Results, Discussion and CC BY 4.0 statement, plus the linked repository README and visible root inventory at commit 7092851cbd9a32116e6453583823d5334ab11f1b. No code, recordings, generated mixtures, supplementary media or trained weights were acquired or executed.

Location has not yet been checked in primary sources.

Location metadata reviewed: Not yet reviewed. Map points are approximate locators, not sample coordinates.

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2021Dataset publication year

Brown-throated parakeet (Eupsittula pertinax) / Parrot contact-call dataset review

Island parakeet contact calls vary across locations ↗

The study analyzes one call type, loud contact calls, rather than the species’ full vocal repertoire. It reports population-level differences in call duration, frequency measures and spectrogram structure/variability across five island populations and mainland Venezuela. The authors explicitly say receiver experiments are needed to test whether these acoustic patterns correspond to receiver responses. This is a useful parrot-specific acoustic-variation study, not a call-meaning or translation result.

Aruba island study region · Bonaire island study region · Curaçao island study region · Isla La Tortuga study region, Venezuela · Isla Margarita study region, Venezuela · Venezuela (country-scale reference)

Observation dates not established here

Evidence, location & limits

Only selected article sections and public metadata were reviewed. The duration-results prose reports 1,319 calls from 92 units, while the 10 rows in Table 5 sum to 1,313 calls from 91 units; the article page does not reconcile this six-call/one-unit difference. Verify against the README and data before replication. The Dryad manifest contains no executable code file although the paper describes a custom MATLAB routine; no separate code repository was audited. Caller/group/session linkage, label provenance, recording conditions, exact mainland site rows, site sensitivity, split structure and receiver-response labels remain unverified. The article-specific reuse license was not established; the dataset-level CC0 record does not license the paper or a derived model. No qualified independent specialist review has occurred.

Reviewed the publisher-indexed abstract, recording approach, Table 1 location statement, frequency/SPCC-PCO methods, duration results and selected discussion passages; this is targeted review, not a complete article audit. The official Dryad API v2 record identifies dataset 77790, version 4 / version ID 147704, dates, dataset-level CC0-1.0 and eight file records with declared SHA-256 digests. The direct publisher fetch returned HTTP 403. The official README API download returned HTTP 401 and its record-page download link returned HTTP 403. No article PDF, README, spreadsheet, audio, supplement or code was acquired; upstream hashes were not locally checked.

The paper names five island study regions and multiple mainland regions in Venezuela. Island markers use only the article’s stated study-area centers rounded to 0.1 degrees; mainland is represented by a generic country-scale reference. No individual recording sites or Dryad workbook coordinates are shown.

  • Aruba island study region · Study region

    The article identifies Aruba as an island study region and says island coordinates are study-area centers. The published center is rounded to one decimal for an approximate literature marker, not a recording or animal point.

    Location source ↗ · Table 1, Recording sites; Aruba coordinate identified as study-area center
  • Bonaire island study region · Study region

    The article identifies Bonaire as an island study region and says island coordinates are study-area centers. The published center is rounded to one decimal for an approximate literature marker, not a recording or animal point.

    Location source ↗ · Table 1, Recording sites; Bonaire coordinate identified as study-area center
  • Curaçao island study region · Study region

    The article identifies Curaçao as an island study region and says island coordinates are study-area centers. The published center is rounded to one decimal for an approximate literature marker, not a recording or animal point.

    Location source ↗ · Table 1, Recording sites; Curaçao coordinate identified as study-area center
  • Isla La Tortuga study region, Venezuela · Study region

    The article identifies Isla La Tortuga as an island study region and says island coordinates are study-area centers. The published center is rounded to one decimal for an approximate literature marker; individual recording sites remain unreviewed and are not mapped.

    Location source ↗ · Table 1, Recording sites; Isla La Tortuga coordinate identified as study-area center
  • Isla Margarita study region, Venezuela · Study region

    The article identifies Isla Margarita as an island study region and says island coordinates are study-area centers. The published center is rounded to one decimal for an approximate literature marker, not a recording or animal point.

    Location source ↗ · Table 1, Recording sites; Isla Margarita coordinate identified as study-area center
  • Venezuela (country-scale reference) · Study region

    The article reports multiple mainland study regions in Venezuela. The marker is GeoNames’ generic country reference rounded to one decimal, not a mainland site, recorder or animal coordinate; no individual locations are inferred.

    Location source ↗ · Methods and Table 1, mainland Venezuela recording regions; GeoNames feature 3625428 for country reference

Location metadata reviewed: 2026-10-25. Map points are approximate locators, not sample coordinates.

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2022Scientific Reports 12:21966; published 2022-12-19

Monk parakeet; cross-species bioacoustics method / Bioacoustic detection and classification

ANIMAL-SPOT: parrot call detection is not translation ↗

ANIMAL-SPOT is a staged, species-specific acoustic detection/classification framework, not one zero-shot animal-language model. Its paper reports recording-level partitioning (samples from a recording stay within one split) and a default 70/15/15 train/validation/test split. For target/noise detection, it reports mean test-set accuracy of 97.9% (range 94.5–99.8%) and, separately, an average AUC of 95.9% (range 91.7–99.1%) across 33 additional unseen animal-specific recordings. In a separate monk-parakeet task, the paper reports 92.7% call-type accuracy on human-labeled test samples and 88.4% on detector-selected segments. These are upstream-reported results, not Talk2Nature measurements; acoustic class prediction does not establish behavior, receiver comprehension or meaning.

No single mapped site

Observation dates not established here

Evidence, location & limits

The recording-level split supports a held-out-recording interpretation, but the article does not state that individual animals are held out; the repository's YEAR+TAPENAME grouping guidance also does not prove individual separation. Edmond metadata lists one public 547,403,503-byte V2.0 archive, but its internal contents and split files remain unreviewed, and the deposit is described as target/noise data rather than the paper's separate call-type corpus. The API's fileAccessRequest=true flag is not itself an access-request gate because the file is marked restricted=false; official Dataverse documentation says the flag is ignored for unrestricted files. Edmond separately requires acceptance of its terms and the dataset license for downloading, and the current Terms of Use does not separately explain the external-downloader scope; no terms were accepted. Metrics and supplementary split tables were not reproduced or independently checked. The work does not show one zero-shot model that translates across species, and it is not evidence of companion-parrot or phone-microphone performance.

Read the full publisher article text, including the target/noise and call-type methods, preprocessing, split policy, results, discussion and ethics/data-collection description, alongside Tables 1 and 3 from the earlier targeted full-text review. The paper states that samples from one recording stay in only one split, with a default 70/15/15 train/validation/test split; it does not establish individual-animal holdout. It reports mean 97.9% test-set accuracy (range 94.5–99.8%) and, separately, 95.9% average AUC (range 91.7–99.1%) across 33 additional unseen animal-specific recordings. Monk-parakeet call-type accuracy is 92.7% on human-labeled test samples and 88.4% on detector-selected segments. Re-read Edmond V2.0 API metadata on October 23, 2026: file restricted=false, fileAccessRequest=true, dataset-level CC BY 4.0 and the same provider MD5. The official Dataverse 6.11 API documentation says access-request settings are ignored when a file is unrestricted. Edmond's own download guide separately requires acceptance of its terms and dataset license; the current Terms' MPS-associated service wording still does not explicitly clarify its application to external downloaders of existing public datasets. The supplement's split tables, exact V2 archive manifest and individual/session recurrence remain unreviewed. No data, code or model was acquired or run; no terms were accepted and metrics were not reproduced.

This is a software and evaluation-method review; it does not establish a verified study location for a single animal cohort.

Location metadata reviewed: 2026-10-13. Map points are approximate locators, not sample coordinates.

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2022Source year

Parrots / Community survey

Parrot owners can contribute; species still matter ↗

The authors received 898 voluntary survey responses and retained a cleaned sample of 877 companion parrots across 33 genera and 73 species after quality checks and excluding birds not reported to mimic human sounds. Respondents said sampled parrots sometimes (36%) or frequently (53%) used mimicked vocalizations in appropriate human contexts without prompting. These are caregiver reports, not independently observed events or receiver tests.

Location awaiting review

Observation dates not established here

Evidence, location & limits

Recruitment used email and social media with parrot advocacy, care, and interest groups and was explicitly biased toward birds that mimic human sounds. The survey asked respondents for their names and email addresses; although the published data item declares CC BY 4.0, the XLSX has not been inspected, so presence or absence of identifiers, consent scope, and privacy suitability are unknown. The contextual-use measure is owner-assessed, not a timestamped recording or controlled behavioral response. This dataset is not an acoustic corpus, decoded vocabulary, or validated training set.

Full main article text, including methods, results, discussion, data-availability and rights statements, reviewed through the Europe PMC full-text XML. Official Figshare v4 API metadata and its single-file manifest were checked. The supplementary questionnaire (Appendix 1), workbook, and raw responses were not inspected or acquired.

Location has not yet been checked in primary sources.

Location metadata reviewed: Not yet reviewed. Map points are approximate locators, not sample coordinates.

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2022Source year

Parrots / Vocal development study

Parrotlet babbling is vocal development, not call translation ↗

The study reports vocal babbling and repertoire development in free-ranging green-rumped parrotlet nestlings of both sexes. The paper describes a corticosterone-related repertoire difference and says the babbling repertoire was not yet integrated into functional contexts; this is developmental biology, not evidence of call translation.

Guárico region, Venezuela

Field work: 2017–2018

Evidence, location & limits

One wild species and a nestling developmental stage were studied at Hato Masaguaral in Guárico, Venezuela, during 2017–2018. The current Dryad API lists version 6 with 2.56 GB of audio bouts, a 43.91 MB spreadsheet of spectrographic measurements and a 4.75 KB README; per-file sizes and SHA-256 values are recorded in the resource catalog. The study includes corticosterone supplementation, so it is not a purely passive observation dataset. The README bytes, measurement schema, individual/session joins, label provenance, audio-video synchronization, treatment groups, split design and model performance were not inspected. No receiver-response or controlled semantic-decoding result is established by the reviewed material. Dryad's current terms and policy state that published datasets are made public under CC0, and no record-specific exception is displayed; this dataset-level license evidence does not establish scientific fit, privacy/welfare suitability or model admission. The article reuse license is separate and was not verified.

Targeted PMC-indexed excerpts of the primary paper's abstract, conclusion and data-access statement; the exact Dryad dataset landing page, public API dataset/version/file records (v6, version record 180872), current End User Terms and Publication Policies; and an indexed excerpt from the official KBA location factsheet. Dryad's published policy states accepted datasets are made public under CC0, with no separate exception shown on this record. API file metadata supplied exact IDs, sizes and SHA-256 digests. An unauthenticated byte-range request to the API download route returned 401; the web file stream returned HTML rather than the file. No paper PDF, supplement, dataset file or audio was downloaded or fully reviewed.

The Dryad Methods name Hato Masaguaral Research Station in Guárico. The map uses a one-decimal regional marker; it does not identify nests or recording structures.

  • Guárico region, Venezuela · Study region

    The paper and Dryad record place the study at Hato Masaguaral in Guarico. The KBA factsheet's indexed excerpt gives broader-site central coordinates; the public marker is rounded to one decimal and labeled as a region, not an exact nest or recording site.

    Location source ↗ · KBA factsheet, central coordinates; paper/Dryad Methods identify the study region

Observation-date source ↗ · Dryad record, Methods

Location metadata reviewed: 2026-10-11. Map points are approximate locators, not sample coordinates.

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2022Source year

Farm animals / Study and dataset

Pig calls: inspect what the labels actually mean ↗

The paper analyzes calls across production contexts. The open Soundwel subset contains 6,888 calls in 17 categories.

Location awaiting review

Observation dates not established here

Evidence, location & limits

The deposit excludes restricted slaughterhouse recordings. Context-associated valence is not a translated sentence or validated household emotion detector.

Primary indexed abstract/results excerpts and repository description/API; full methods not reviewed.

Location has not yet been checked in primary sources.

Location metadata reviewed: Not yet reviewed. Map points are approximate locators, not sample coordinates.

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2023Source year

Birds / Evaluation method

BIRB: test on the sounds your app will actually hear ↗

BIRB tests retrieval from passive soundscapes using examples from focal bird recordings, including rare and held-out species.

No single mapped site

Observation dates not established here

Evidence, location & limits

These are retrieval and species tasks. They do not validate behavior labels, conversation or performance on a phone beside a pet.

Primary paper v2 introduction, benchmark overview and Table 1; repository BIRB entry. Detailed metrics and full experiments still need review.

This note covers a tool, benchmark, method or claim review rather than one verified observation site.

Location metadata reviewed: 2026-10-03. Map points are approximate locators, not sample coordinates.

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2023Source year

Parrots / Neural and bioacoustic study with a separate data deposit

Budgerigar vocal signatures: caller identity, not call meaning ↗

The full article used 16 adult budgerigars, with eight in the contact-call cohort and five in the neural-inactivation experiment. Playback of colony-noise segments elicited 180–1,612 contact calls per bird per day in the eight-bird cohort. A VAE representation and SVM identified callers at over 90% accuracy in a balanced random 80/20 call-level split, repeated 50 times; accuracy remained about 90% for two shared call types. A classifier trained at week 3 and tested at week 15 fell from about 90% to 40%, consistent with call change over time. In the separate five-bird experiment, birds still responded to individual calls during AFP inactivation, although response rate fell in three birds and latency effects varied. Inactivation altered call acoustics and reduced individual discriminability. These are bounded acoustic identity and production findings, not decoded meaning.

Location awaiting review

Observation dates not established here

Evidence, location & limits

The reported identity classifier randomly split calls from the same known birds; no held-out-bird test is reported, so its accuracy is not generalization to unseen animals, species, sites or devices. The study used small captive groups, controlled loudspeaker playback, directional laboratory microphones and invasive neural interventions. Receiver responses were measured in five birds and were not a test of name comprehension or call meaning. The official data page associates ColonyNoiseCalls with Figure 2, the eight-bird contact-call identity analysis; the single inspected file has no verified mapping to a specific bird, session or condition and no independent behavior-label field. The paper's reviewed methods do not state a digitization rate, so the sample's stored 44,101 value remains unexplained. The dataset separately declares CC BY 4.0; custom scripts are available from the lead contact without a verified public license.

Reviewed the full Current Biology article text through NCBI PMC E-utilities (PMCID PMC10799565), including call-elicitation, VAE/SVM methods, results, discussion, animal-care and data/code statements. Rechecked the version-1 Mendeley landing-page description and API documentation against the prior checksum-pinned one-file inspection. No new data files were acquired or parsed; no code/model was run, no reanalysis or independent review was performed.

Location has not yet been checked in primary sources.

Location metadata reviewed: Not yet reviewed. Map points are approximate locators, not sample coordinates.

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2023Source year

Parrots / Vocal learning and sociality study

Budgerigar vocal and social records are not event-aligned ↗

In a 48-bird study, older adults showed reduced vocal diversity over the experiment and older-age flocks had weaker/less dense affiliative ties; measured vocal plasticity and convergence were similar across age classes. The deposit pairs derived call features and social observations at flock/date/block scale, not at synchronized call-event scale.

Las Cruces, New Mexico

Experiment: 14 Dec 2020–27 Apr 2021

Evidence, location & limits

The experiment formed 12 novel flocks of four adult males in two age classes. Individual contact calls were collected in separate 55-minute sessions with each focal bird in a sound-isolation chamber, visually but not acoustically connected to flockmates. Social behavior was filmed in separate 80-minute sessions of these newly formed experimental flocks, not home flocks; early-morning video was sampled for affiliative and agonistic interactions after a 30-minute settling period, while proximity was assessed from snapshots. Dryad's 2024 deposit lists derived call features and social tables with overlapping flock/date/block fields but no original call WAV files or shared call/event timestamps. It cannot support event-level sound-to-behavior prediction. The 12-flock design and age treatment constrain generalization. The README documents an interobserver workbook with a subset recoded by a second observer; the workbook and R analysis scripts were not inspected, and no agreement statistic or reproduced result is claimed.

Reviewed the primary article's animal housing, experimental outline, audio recording and video recording Methods through PMC-indexed full text and a coauthor-hosted article PDF; the official Dryad version history, file inventory and README data dictionary; Dryad reuse guidance; and the official NMSU facility locator. Direct PMC page access encountered CAPTCHA. No Dryad files, raw audio, video, supplement or code were downloaded or executed. The README describes the interobserver workbook, but it was not inspected and agreement was not recalculated.

The study names the NMSU Animal Care Facility; the map shows an approximate city-level marker only.

  • Las Cruces, New Mexico · Study region

    The paper identifies NMSU's Animal Care Facility as the housing site; the university building list locates that facility in Las Cruces. The atlas generalizes to the city and does not mark the facility or animal rooms.

    Location source ↗ · NMSU building list: Animal Care Facility

Observation-date source ↗ · Methods 2(b): Experimental outline

Location metadata reviewed: 2026-10-11. Map points are approximate locators, not sample coordinates.

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2017–2023Data descriptor and follow-up study years

Egyptian fruit bat (Rousettus aegyptiacus) / Audiovisual dataset and context-classification review

Fruit-bat calls and context: a rich dataset, not a translation key ↗

The 2017 archive describes 293,238 Egyptian fruit-bat WAV files recorded at 250 kHz and 16-bit, plus 91,080 labels derived from synchronized video, including caller, addressee, behavior and social context. The data descriptor reports a post-hoc recheck of 435 annotations, with a 4% estimated context-label error rate (95% CI 2.2–5.8%). The reviewed metadata PDF says annotation FileIDs map to FileInfo.csv; its tables describe recording-date ranges, treatment/channel settings and bats that recur across treatments, and define known, unknown and ambiguous caller/receiver codes. This makes a file/animal grouping audit plausible, but not yet verified against FileInfo rows.

Location awaiting review

Observation dates not established here

Evidence, location & limits

The 2016 analysis reported 61% balanced accuracy for four aggressive contexts versus 25% chance, using 14,863 calls from seven adult females. Its vocalization-level cross-validation does not establish generalization to held-out bats. The 2023 follow-up used sequences from three adult females; the context HMM used 326 sequences (2,953 syllables), formed 12,900 n-grams and reported overall balanced accuracy 66 ± 10 under random eight-fold cross-validation. Shuffling syllable order left the score unchanged. These are predictions of observer-coded context, not evidence that bats or humans interpret a call as a message. The 2023 authors say behavioral experiments are needed to establish biological relevance. The laboratory archive used synchronized video, isolated chambers and specialized 250 kHz ultrasound recording; ordinary phone recordings cannot be assumed to match its bandwidth, calibration or capture conditions. The Figshare public item API declares CC0 1.0 for archive item 4556074 and lists one 3,049,199,327-byte ZIP; its payload and embedded rights notices were not inspected. The separate annotation item 4555903 declares CC0 1.0; its 3,286,405-byte file was locally size/MD5 verified. The aggregate audit found 91,080 rows, 87,986 distinct file IDs, no malformed fields and exact Table 4 count matches. These are structural/transcription checks, not independent validation. The PDF does not prove FileInfo row coverage, uniqueness or actual joins; the 31,574,701-byte FileInfo.csv was not fetched. Its API record has no upstream MD5 and the file exceeds the collector's 5 MiB per-file ceiling. Multiple annotations can belong to one file; ambiguous/unknown identities are present. No raw rows are published. No audio, video, FileInfo contents, encodings, code or weights were inspected or run.

Reviewed the 2017 Scientific Data article's abstract, methods, data records, Tables 3–4, technical validation, usage and rights sections; the 2016 Scientific Reports results and classification methods; the 2023 iScience abstract, dataset description, sequence-HMM methods/results, discussion and data statement; the Mendeley Data version 3 description/license and official version 1/2 and 2/3 comparisons; the Mendeley public file-list API documentation and one anonymous version 3 request (HTTP 401; documentation specifies OAuth2); the NLM catalog entry; and direct public Figshare API metadata for archive item 4556074, annotation item 4555903, FileInfo item 4555897 and Metadata.pdf item 4555900. Acquired only the 3.29 MB annotation table with verified size/MD5 for aggregate-only schema/category-count comparison, plus the 367,257-byte metadata PDF for visual review of all five pages; its source API supplies no MD5, so the receipt's SHA-256 is local provenance only. No audio/video, FileInfo.csv, code or model weights were inspected, and no published model result was reproduced.

Location has not yet been checked in primary sources.

Location metadata reviewed: Not yet reviewed. Map points are approximate locators, not sample coordinates.

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2023Source year

Amphibians / Benchmark dataset

Frog choruses: several callers, one recording ↗

AnuraSet describes 27 hours of expert annotations across 42 Neotropical frog species. The paper reports a 70/30 iterative multilabel split grouped by one-minute recording and a 37.8% overall macro-F1 at threshold 0.5; the rare-species group scored 15.7%. This is a species-identification task, not a call-meaning benchmark.

Brazil · Cerrado and Atlantic Forest

Collection across 2019–2021

Evidence, location & limits

The article says the raw data and dataset are CC0, while the versioned Zenodo API record declares CC BY 4.0; reuse rights remain unresolved. The reported split groups 3-second samples by one-minute recording, but does not establish site- or year-held-out generalization. The static evaluator passes sigmoid predictions to BCEWithLogitsLoss for a progress-only loss display; saved metrics are computed separately. Data bytes, weights and outputs were not acquired or reproduced, and no qualified herpetologist has reviewed this note.

Full data-availability and split sections and selected results in the paper; official Zenodo API metadata; README, exact pinned MIT license and evaluator source read as text. No replication or code execution.

The paper describes four sites across Brazil’s Cerrado and Atlantic Forest. One country-level locator represents the multi-site dataset.

  • Brazil · Cerrado and Atlantic Forest · Study region

    The paper describes four sites across Brazil’s Cerrado and Atlantic Forest. One country-level locator represents the multi-site dataset.

    Location source ↗ · Methods: Data Collection and Figure 2 caption

Observation-date source ↗ · Introduction and Methods: Data Collection

Location metadata reviewed: 2026-10-03. Map points are approximate locators, not sample coordinates.

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2023Publication year

Insects / Machine-learning and follower-response study

Honeybee dance records connect movement with followers and food sites ↗

Dormagen et al. combined waggle-phase detection, individual tracking, follower observations and feeder records. The paper reports more than 100,000 detected waggle phases. A manually annotated subset covers 176 videos, 3,278,121 detections from 28,873 tracks and 4,389 bee IDs, including 1,107 waggle phases and 279 follower events. The Zenodo v1.0 API lists 11 deposited files totaling 4,870,358,178 bytes (4.87 decimal GB), alongside more than 46 million 2019 track detections. This is a rare data path connecting a well-defined sender signal to observed receiver behavior and known food-site outcomes.

Berlin study region, Germany

2019 and 2021 recording seasons

Evidence, location & limits

This is not a blank slate: the paper already analyzes waggle dance/follower relationships and feeder outcomes. It spans one observation-hive site across two seasons; the published sample-level cross-validation groups by hour rather than holding out bees or colonies. The 2021 recordings lack marked individual identities. The dataset is specialized tracking and video data, not an ordinary-phone audio corpus. The public record contains fine-scale feeder coordinates, which are not reproduced here. The separately named Berlin2019_followers.csv is 9,171,824 bytes, above the current 5 MiB per-file collector cap; smaller dance/feeder metadata files have not been shown to substitute for the follower outcome. No payload bytes were inspected. CC BY 4.0 in the Zenodo API covers the deposit, not linked software or model artifacts.

Targeted review of the PNAS Nexus abstract, communication background, colony and camera setup, annotation and hourly sample grouping, follower/feeder results, data statement and article license; on 2026-10-25, rechecked the complete Zenodo v1.0 API file manifest and dataset license. The 11 listed sizes sum to 4,870,358,178 bytes; the named follower CSV is 9,171,824 bytes. No data, video, code or model was downloaded, locally hashed, inspected or executed.

The primary article identifies Berlin as the observation-hive study region. This coarse marker does not expose the hive or feeder coordinates listed in the data record.

  • Berlin study region, Germany · Study region

    The primary article identifies both recording seasons as Berlin study campaigns; exact hive and feeder coordinates are intentionally omitted.

    Location source ↗ · Methods, Colony setup: Berlin 2019 and Berlin 2021

Observation-date source ↗ · Methods, Colony setup: Berlin 2019 and Berlin 2021

Location metadata reviewed: 2026-10-24. Map points are approximate locators, not sample coordinates.

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2023Published 27 July 2023

Monk parakeet and yellow-naped amazon / Parrot acoustic identity and social-scale comparison

Monk parakeet calls carry individual identity information ↗

The authors report stronger acoustic convergence at the individual than site scale in native-range Uruguay and introduced U.S. monk parakeet populations. Their yellow-naped amazon comparator showed stronger convergence at the regional-dialect scale. This is evidence about acoustic identity structure and a proposed identity-signaling framework, not decoded call meaning or demonstrated receiver comprehension.

Uruguay · United States · Costa Rica

Discontinuous source recordings: Costa Rica 1994; United States 2004–2019; Uruguay 2017

Evidence, location & limits

The article’s supervised random forest was trained on 156 calls from 8 known monk parakeets using 114 selected features. The reported 97.44% accuracy (95% CI 93.57–99.30) is training accuracy. The remaining 73 individual-scale calls from 9 other birds were used to produce proximity matrices and inspect clustering patterns; the paper does not report that 97.44% as held-out unseen-caller accuracy. The study reports 229 individual-scale calls and 1,353 site-scale calls. The Figshare README says its monk-parakeet selection table contains 1,596 rows, of which 1,582 were used in the paper. The pinned v1.0.0 site-scale script explains the difference: 14 site-scale calls from birds also represented in the individual-scale set are removed before the scale comparison. This source-level reconciliation was not independently recomputed from the selection CSV or WAVs. The full supplement, exact file-level rights/provenance, remaining code and dependencies were not audited. Acoustic similarity cannot by itself show what a caller intends or what a receiver understands.

Reviewed the PLOS article’s sampling, methods, results, discussion, ethics, and linked-resource sections. Refreshed the Figshare v2 API and streamed its README.md file 41233869 for review; it was not retained. Resolved the GitHub v1.0.0 tag to commit 1f7031dc996c79914ccf5d042181f947793fb459 and read the site-scale selection R Markdown file at that revision without retaining or executing it. The code explains the 14-row selection difference. The PLOS supplement, remaining code/dependency workflow, selection CSV, and audio were not reviewed. No dataset files or audio were acquired; no code was executed.

Country-scale study regions only: Uruguay and the United States concern monk parakeets; Costa Rica is a 1994 yellow-naped amazon comparator. Map points are approximate editorial locators, not recording coordinates.

  • Uruguay · Study region

    Native-range monk-parakeet contact calls were recorded in 2017 at 37 sites across seven departments. The atlas shows only the country-scale study region.

    Location source ↗ · Methods 2.2: Recording contact calls
  • United States · Study region

    Introduced-range monk-parakeet contact calls were sampled at 26 sites across five states in 2004, 2011, 2018, and 2019. The atlas shows only the country-scale study region.

    Location source ↗ · Methods 2.2: Recording contact calls
  • Costa Rica · Study region

    The yellow-naped amazon comparator uses previously published contact calls recorded in 1994. This is a separate species and historical comparator, not a monk-parakeet site.

    Location source ↗ · Methods 2.10: Comparing hierarchical mapping patterns with another parrot species

Observation-date source ↗ · Methods 2.2 and 2.10

Location metadata reviewed: 2026-10-13. Map points are approximate locators, not sample coordinates.

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2023Software paper (2023); current code release reviewed separately

Cross-species bioacoustics / Research software and strongly labeled bird-soundscape dataset

OpenSoundscape: bioacoustic tools, not animal translation ↗

OpenSoundscape is a Python research toolkit for manipulating audio and spectrograms, working with Raven time-frequency annotations, preparing labels, training or applying acoustic classifiers, detecting periodic calls, and localizing sounds from synchronized recorders. Its 2023 paper describes version 0.9.1; the current official release page lists v0.13.2. This is useful infrastructure for acoustic observation and researcher workflows, not a model that translates animal communication. The linked Dryad dataset is a distinct resource: its current landing page reports 385 minutes of bird soundscape recordings across four days, 48 species and 16,052 strong annotations. These labels support bounded detection or classification tasks; they do not establish call meaning or receiver understanding.

No single mapped site

Observation dates not established here

Evidence, location & limits

The tool is a Python analysis package, not a beginner mobile app, persistent recording service, or general animal-language system. The tutorial's pinned package version trails the newest release, so any reproduction must pin and verify the version actually used. The tagged MIT code license does not license dependencies, model-zoo code, checkpoints, tutorial media, or datasets. Dryad's current policy says repository data are CC0, but the reviewed item page did not expose an item-level license field; reconfirm the exact version terms before reuse. The dataset's four recording days and one named reserve do not demonstrate broad geographic or individual-level generalization. No files, labels, or data splits were independently inspected.

Reviewed the paper abstract, software overview, annotation and localization functionality, examples, and stated scope; the official v0.13.2 release and tagged MIT license; the official annotation tutorial; and the Dryad dataset landing page and repository-wide license policy. The article describes v0.9.1, the repository lists v0.13.2 as its latest release, while the stable annotation tutorial still pins 0.13.1. No package, code, model, recording, archive, or annotation row was downloaded, installed, or run. The publisher marks the article Open Access, but its exact reuse license was not visible in the reviewed page. No independent software or biological review occurred.

This note reviews software and a dataset landing page, not verified participant or field-observation coordinates.

Location metadata reviewed: 2026-10-13. Map points are approximate locators, not sample coordinates.

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2023Source year

Birds / Bioacoustic identity study

Monk parakeet voiceprints test caller identity, not meaning ↗

A study of 229 monk parakeets tests caller identity across call types; it does not predict behavior or decode meaning.

Barcelona region, Spain

Recording windows: 27 Oct–19 Nov 2020 and 31 Oct–30 Nov 2021

Evidence, location & limits

The research target is individual identity, not behavior, context or semantics. Figure 4 describes splitting recordings and predicting marked bird IDs across call types; this appears to test transfer for known individuals, not generalization to unseen birds (an interpretation of the design). In 2020, annotation_ref links annotation rows to Raven selections, and the README says multiple selections can share one reference; rows therefore are not necessarily independent call-level labels. The free-text behavior field describes behavior during or just before vocalizations and can contain multiple values; sequence context is documented separately. The 2021 schema differs. No observer-reliability result was inspected. No Edmond archive/member or deposited audio was acquired. The Edmond API's dataset-level fileAccessRequest flag does not resolve any individual file's access status.

Reviewed the final-article abstract and targeted methods/figure text surfaced by the primary PMC index, plus the official pinned GitHub README and the existing hash-pinned 2020 annotation sample. Recomputed the sample's Git blob SHA-1 and SHA-256; the Git blob matches the pinned GitHub file. Rechecked Edmond's released v1 API, current Data Access & Usage guide, ZIP-viewer FAQ, Terms of Use and official July 20, 2026 terms-update notice. No Edmond terms were accepted and no deposit archive/member was downloaded. Direct full-text retrieval was CAPTCHA-limited; the ANU repository PDF fetch timed out. The supplement, full statistical/code workflow and raw audio were not reviewed or run.

A wild monk-parakeet study area; this is not the location of a Talk2Nature companion-parrot pilot.

  • Barcelona region, Spain · Study region

    The study names Parc de la Ciutadella and surrounding areas in Barcelona. The atlas uses the broad city region and does not reproduce nest locations.

    Location source ↗ · Methods 2.1: Study system

Observation-date source ↗ · Methods 2.2: Data collection

Location metadata reviewed: 2026-10-04. Map points are approximate locators, not sample coordinates.

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2023Source year

Plants & fungi / Mechanism study

Plants have channels beyond sound ↗

Laboratory Arabidopsis imaging connects particular airborne leaf volatiles with calcium responses and defense-related gene expression.

Location awaiting review

Observation dates not established here

Evidence, location & limits

The measurements use specialized reporters and imaging. Ordinary phone video cannot reveal these intracellular signals or decode a plant’s intention.

Primary abstract, introduction and selected results/methods passages reviewed; supplementary data not reanalysed.

Location has not yet been checked in primary sources.

Location metadata reviewed: Not yet reviewed. Map points are approximate locators, not sample coordinates.

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2023Source year

Plants & fungi / Recording study

Plant acoustic emissions: detection is not communication ↗

Khait et al. report airborne ultrasonic emissions from stressed tomato and tobacco in an acoustic box and greenhouse, and classify plant condition from recordings. The system used paired CM16 ultrasound microphones per plant at 500 kHz, with a 20 kHz high-pass filter; an event counted as plant-associated only when both microphones aimed at that plant recorded it. They report leave-one-plant-out evaluation: a 1-hour greenhouse dry-versus-irrigated classification reached 84% balanced accuracy, and a two-range soil-moisture classification in a nine-day dry-down of 23 tomato plants reached 81%. These are emission and physiological-state results, not decoded messages or tested receiver responses.

Tel Aviv region, Israel

Observation dates not established here

Evidence, location & limits

The experiments used specialized ultrasonic hardware and controlled acoustic-box/greenhouse settings; ordinary phone microphones are not an equivalent sensor. The plant-held-out folds reduce leakage from repeated sounds of the same plant, but do not establish generalization to new sites, devices, species or field noise. The paper's 3–5 m animal detection range is estimated from reported sound levels and published hearing thresholds, not measured animal response. Nardini et al.'s 2024 Opinion places acoustic emissions in a long history of plant physiology and discusses xylem cavitation and other physical mechanisms; it says ecological receiver use still needed evidence at that time. The later moth study in source 16 supplies a bounded receiver-response result in one moth/plant setting, not evidence of plant intention or a general language. No independent reproduction or qualified plant-acoustics review has been obtained. Dryad lists the recordings under its CC0 repository policy, but the archive contents were not inspected; the Zenodo classifier record has no license stated.

Read the main 2023 Cell article and its full STAR Methods, including the recording, preprocessing, model evaluation and results sections; checked the relevant figure legends and selected supporting captions. Read the 2024 Opinion's historical framing, mechanism discussion, receiver-evidence conclusion and open questions. Also checked the exact Dryad record, Dryad's live CC0 policy and the linked Zenodo v1 classifier record. The Cell publisher endpoint was inaccessible, so its linked full-paper copy was used. No audio, archive, code or model was downloaded or run; no result was independently reproduced.

The paper places the acoustic box and greenhouse at Tel Aviv University. This is a study-region locator; the recordings used specialized ultrasound equipment, not an ordinary phone microphone.

  • Tel Aviv region, Israel · Study region

    The recording protocol places the acoustic box at TAU and explicitly names Tel-Aviv University for the greenhouse recordings.

    Location source ↗ · STAR Methods: Recording protocol, acoustic box and greenhouse; PDF page 12 / printed e2

Location metadata reviewed: 2026-10-13. Map points are approximate locators, not sample coordinates.

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2024Source year

Companion animals / Machine-learning study

What bark classification can tell us ↗

The study reports 62.18% accuracy for Wav2Vec2 pretrained on 960 hours of human LibriSpeech, versus a 56.37% majority baseline, on a selected four-class bark/context task. The result is prediction of association with induced study situations, not a test of what dogs understand or mean.

Tepic region, Mexico · Puebla region, Mexico

Observation dates not established here

Evidence, location & limits

The 74 dogs were recorded in owners’ homes in Tepic and Puebla during designed exposures such as stranger, play and owner-related situations. Sound segments were manually assigned one of 14 labels from information associated with the stimulus; 8,755 of 16,789 segments are non-dog sounds. The four-class grounding subset mixes vocal form (bark, squeal, grunt) with stranger-related exposure. Dog-held-out folds are reported, but the paper does not describe household, owner, session, stimulus-instance or recording-condition grouping. This is not proof that those groups leaked; their independence cannot be checked from the paper. The paper does not report an independently observer-coded behavior-label stream for these segments or a receiver-response test of the calls. No replication was performed. Results do not establish broad emotion inference, semantics or translation.

Full seven-page ACL Anthology paper reviewed on 2026-10-13, including Dataset, segmentation/annotation, all four task sections, five results tables and Limitations. No data, code or weights were acquired or executed; no result was reproduced.

Two recording regions in one study, not two independent datasets. Household locations are not published.

  • Tepic region, Mexico · Study region

    The dataset section names recordings at dog owners’ homes in Tepic and Puebla, Mexico. Only the two broad city regions are shown.

    Location source ↗ · Section 3: Dataset; PDF page 2, printed page 16481
  • Puebla region, Mexico · Study region

    The dataset section names recordings at dog owners’ homes in Tepic and Puebla, Mexico. Only the two broad city regions are shown.

    Location source ↗ · Section 3: Dataset; PDF page 2, printed page 16481

Location metadata reviewed: 2026-10-13. Map points are approximate locators, not sample coordinates.

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2024Source year

Companion animals / Study announcement

Learned buttons and receiver responses ↗

Research-team reporting describes appropriate responses by trained dogs to selected soundboard words.

Location awaiting review

Observation dates not established here

Evidence, location & limits

This is evidence about responses to learned cues, not unrestricted conversation or a natural canine-language translation.

University study report and vendor description reviewed; primary paper review pending.

Location has not yet been checked in primary sources.

Location metadata reviewed: Not yet reviewed. Map points are approximate locators, not sample coordinates.

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2024EUSIPCO 2024 proceedings; arXiv manuscript v1

Egyptian fruit bat (Rousettus aegyptiacus) / Machine-learning study

A 2024 model study predicts bat context—with important limits ↗

Triantafyllopoulos et al. trained models to predict 11 video-coded interaction contexts from Egyptian fruit-bat calls. Their best reported result was 33.3% unweighted average recall; this predicts observed context, not message meaning or receiver comprehension. A fresh checksum-verified audit of the paper-described subset matches 35,074 annotations. Strict source-file-plus-participant graphs remain highly connected (largest 95.56%), but a role-aware caller diagnostic excluding 122 mixed-caller source files retains 34,815 annotations (99.26%) and leaves every context represented by 8–41 callers. This makes a caller-held-out test plausible for review, not validated: the paper already reports subject-independent folds, exact membership is unverified, caller identities can appear as addressees in development, and session/chamber/treatment factors remain unjoined. No split or model was created.

Location awaiting review

Observation dates not established here

Evidence, location & limits

The paper’s subject-independent test split is a positive reported safeguard, but Talk2Nature has not inspected the partitions or verified which identities, roles, sessions, dates, microphones, chambers or treatments cross folds. The training/validation split inside each development set ignores identity. Confidence intervals bootstrap individual predictions rather than clustered animals or sessions. Class frequencies are unequal and grooming recall is zero. Context is an observer-coded group/action category, not a test of receiver interpretation. The 2026-10-23 public-code search covered the first author’s 13-repository owner inventory and an exact-title GitHub repository search, not coauthor/lab/archive locations; a Zenodo API search was inaccessible and yielded no negative finding. The pinned Earth Species notebook snapshot predates the EUSIPCO paper and therefore is not its promised release; a separate coauthor/lab/archive release may still exist. The repository root inventory has no visible LICENSE, and notebook 99a states code license “to be linked”; it declares dataset CC BY 4.0, which is not reconciled with Talk2Nature’s direct item-level Figshare records or derived archive-copy provenance. No independent reproduction or specialist review exists. The paper reports ImageNet-pretrained vision weights, not a bioacoustic foundation model. The arXiv v1 manuscript declares CC BY-NC-ND 4.0; proceedings-copy reuse terms were not separately reviewed. The identity graph covers the complete annotation table, not the 35,074 records used by the 2024 paper, and cannot establish whether its three folds isolate all interacting bats, emitters only, sessions, channels, treatments or chamber backgrounds. Metadata.pdf provides treatment and subject context, but the necessary per-file timestamp/channel join is in a separate 31,574,701-byte FileInfo.csv item with no upstream MD5; it was not fetched. The aggregate graph is not an independent split or leakage test. The 2026-10-24 filtered-subset audit reproduces the published row count, not the authors' exact selected-row manifest. Under source-FileID plus positive-known-participant grouping, nine components remain and the largest contains 95.56% of the filtered annotations. Under source-FileID plus positive-emitter grouping, ten components remain and the largest contains 95.31%. These strict component groupings are too concentrated for a balanced whole-component holdout. A separate caller-support diagnostic excludes 122 files with multiple known callers and retains 34,815 of 35,074 annotations (99.26%); it is descriptive support for review, not a split. It does not prevent the same animal appearing as an addressee in development data. Unknown participant roles may hide more links, and the available annotation table does not resolve session, date, channel, treatment or chamber grouping. No model was run.

Reviewed all five pages of the official EUSIPCO 2024 proceedings paper and the official arXiv v1 record/manuscript, focusing on dataset filtering, evaluation protocol, model setup, metrics, class errors, data source and license. Also reviewed the first author’s linked public GitHub profile and the pinned Earth Species Library snapshot at commit 6382c52bf553569528e0a790efe16e5ae42f0094: root inventory, bat README and displayed task/split/license sections from notebooks 01, 02, 99a and 99b. No code, split files, raw media, model weights or supplements were downloaded or run. On 2026-10-23, the first author’s live GitHub owner-repository API again listed 13 public repositories (newest update 2025-10-20), and a GitHub repository search for the exact paper title returned no match. This remains a bounded search, not proof that code or splits are unavailable elsewhere. A Zenodo API search was inaccessible from this environment and is not treated as a negative result. The acquired five-page Figshare Metadata.pdf item 4555900 was visually reviewed across Tables 1–4. It lists 20 treatment periods with date ranges, group composition and channels; subject IDs, mother/date-of-birth fields; and an annotation schema with both emitter and addressee. The already-acquired 91,080-row Annotations.csv item was rechecked with an aggregate-only participant-pair audit. No raw annotation rows or IDs were exported. The 31.6 MB FileInfo.csv item and the paper’s exact fold files remain unacquired/unavailable. On 2026-10-23, the checksum-verified Annotations.csv was filtered using the paper-described context, emitter and duration rules. The aggregate-only script matches the reported 35,074-row count and summarizes file-plus-emitter and file-plus-all-known-participant group topology; it exports no row or identity values. No audio, FileInfo.csv, paper fold files, model code or model weights were acquired or run. On 2026-10-23, the official St Andrews group page was reviewed for bat vocal-communication expertise, and the TAU profile plus Nature 2025 author list were checked solely to screen fit and disclose a separate Vernes–Yovel coauthorship. No reviewer was contacted or selected. On October 24, 2026, the official Figshare API and exact CC0-declared annotation file size/MD5 were rechecked, then the aggregate audit was regenerated. The new caller-support diagnostic excludes 122 mixed-caller files and retains 99.26% of the filtered annotations; it creates no split.

Location has not yet been checked in primary sources.

Location metadata reviewed: Not yet reviewed. Map points are approximate locators, not sample coordinates.

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2024NeurIPS 2024 Datasets and Benchmarks Track

Birds, mammals, insects, reptiles and amphibians / Large-scale animal-audio classification dataset

iNatSounds scales species recognition, not animal meaning ↗

iNatSounds is a substantial broad animal-audio resource: the paper reports about 230,000 recordings (around 1,200 hours) contributed by more than 27,000 recordists, covering over 5,500 species. The 2024 README reports 5,569 species across birds, insects, amphibians, mammals and reptiles. Its task is file-level species classification; every recording has a single species label. This makes it useful for studying acoustic recognition and pretraining, not event-level meaning, animal behavior or whether a receiver understood a call.

No single mapped site

Observation dates not established here

Evidence, location & limits

The paper calls the labels weak: the target sound may be absent from a particular short window, and other taxa or background may be present. It advises evaluating on strongly labeled downstream datasets rather than relying on iNatSounds alone. The paper also notes geographic sampling bias toward the United States and Europe. The README lists roughly 133 GB of audio across the three archives and exposes date, latitude, longitude and location-uncertainty fields; sizes are provider-reported and no location audit was performed. Dataset download terms permit non-commercial research and education only and prohibit distributing recordings. Underlying iNaturalist media have contributor-selected licenses, including non-commercial, other Creative Commons and all-rights-reserved items. The code repository reports MIT for code only; linked model-weight terms and paper reuse terms remain unverified. No model, audio or annotation was inspected locally, and no Talk2Nature result exists.

Reviewed the paper abstract and targeted dataset-construction, evaluation, weak-label and assets/consent passages; the complete paper was not exhaustively reviewed. Reviewed the complete 2024 dataset README at commit bd52ecde5d9d7434f6167bbac823d152c3823ccd, including class counts, single-species file labels, location/license fields, ToU and provider-listed archive sizes; the current iNaturalist media-license FAQ; the pinned code repository README and MIT SPDX metadata; and the author-linked model folder pointer. No audio, annotation archive, source code, dependencies or weights were downloaded or run.

This global compilation has no single field site or representative point for the literature atlas. The paper documents geographic sampling bias, and the dataset schema includes per-recording location metadata; no individual coordinates or locations were reviewed or reproduced.

Location metadata reviewed: 2026-10-25. Map points are approximate locators, not sample coordinates.

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2024Source year

Parrots / Animal-computer interaction study

What parrot touchscreen research can—and cannot—tell us ↗

In the study's specific 10.1-inch tablet task, 17 trained pet parrots completed 4,720 selected target touches (8,995 counting follow-on taps). Hit rate increased with target width from 19.29% at 40 dp to 47.08% at 130 dp; the study found no significant effect of target distance and no detected difference in touch-point precision by target width. The authors' 300 ms rule classified 45.73% of taps as multi-taps. This is touchscreen-ergonomics evidence, not a communication test.

Location awaiting review

Observation dates not established here

Evidence, location & limits

Twenty adult home-kept parrots entered and 17 completed testing after gradual training; eligibility required prior comfort with screens/objects and balloon-pop-style games, and caregivers needed animal-training experience and time. Two birds did not reach stable engagement within six weeks and one showed slight aggression. Testing used a 10.1-inch Galaxy Tab A at a fixed angle and size-specific perch distances, not phones or an ordinary untrained interaction. Hit-rate differences may reflect larger target area, not improved precision; the exact-width comparison found no precision difference. The proposed touch-and-retreat account is an interpretation of video observations, not a separately quantified mechanism. Caregiver survey responses are self-report. The University of Glasgow record lists CC BY-NC-ND 4.0 and 'Data Availability Statement: No'; raw data and a separate code license were not established. No independent specialist review or replication has been completed.

On October 14, reviewed the full article text from the 16-page published PDF hosted by the University of Glasgow, from the abstract through the conclusion, including the caregiver survey/pilot, participant criteria, ethics, protocol, data cleaning, results, limitations and design guidelines. Figures were not visually reanalyzed; no raw data, code, supplements or independent analysis were available or inspected.

Location has not yet been checked in primary sources.

Location metadata reviewed: Not yet reviewed. Map points are approximate locators, not sample coordinates.

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2024Source year

Birds / Interface study

Giving parrots a choice to connect ↗

In a six-month, nine-parrot home study, Table 2 lists 65 parrot-triggered live calls and 40 playback calls out of 108 possible in each phase. Recalculation from the displayed counts gives live-minus-playback differences of +50, +17, +75, +17, +8, -8, -8, +33 and +25 percentage points for P1–P9. The printed row is reversed for seven birds and appears to swap the P3/P5 entries. The authors report a live-phase advantage, but the published duration totals do not reconcile across the text and Table 3; the duration magnitude remains unresolved.

Location awaiting review

Observation dates not established here

Evidence, location & limits

The live phase always came first, so condition order was not counterbalanced; reciprocal calls, caregiver cues and variable home devices also complicate interpretation. Table 2 labels the comparison ‘Live minus playback,’ but the printed values correspond to playback-minus-live for P1, P2, P4 and P6–P9; P3 and P5 also appear transposed relative to the displayed counts. No underlying data were available to verify author intent, and no correction was found in the current University of Glasgow record or Crossref relation metadata; that check is not exhaustive. The Data Analysis text reports 561 live-phase and 142 playback minutes, while summing Table 3's displayed per-parrot cells gives about 239 and 149 minutes, respectively; no underlying data were available to resolve this. The inferential tests were not independently recomputed. Engagement is not a direct welfare, comprehension or language measure.

Published paper pages 3–12 reviewed for participant screening/ethics, protocol, analysis, results and limitations. Page 9 was rendered and Table 2's P1–P9 differences recalculated from the displayed session counts. The current University of Glasgow deposit record and Crossref DOI relation metadata were checked. No raw-data reanalysis or replication.

Location has not yet been checked in primary sources.

Location metadata reviewed: Not yet reviewed. Map points are approximate locators, not sample coordinates.

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2021–2024Primary studies reviewed

Multiple taxa: common nightingale and bats / Mobile bioacoustics methods comparison

Phones can record wildlife, but they are not interchangeable sensors ↗

Two different studies show that smartphones can support bounded wildlife-acoustic tasks, but they do not create a uniform sensor. Jäckel et al. compared 5,679 Berlin citizen-science nightingale recordings with expert recordings: 53% of citizen recordings were categorized as complete songs versus 100% of expert recordings, and signal-to-noise ratios were lower for citizen recordings. In a small controlled playback comparison, estimated frequency limits varied by device. The authors nevertheless considered the citizen recordings useful for questions based on relative song-type occurrence, while cautioning about exact acoustic measurements. Gili et al. tested built-in microphones for selected 8–24 kHz bat calls. Their field/pilot work covered 35 phone models and nine bat species, but found device-model variation, occasional aliasing and loss of calls near the upper detection limit; the authors recommend preliminary detectability checks and expert verification of at least some identifications.

No single mapped site

Observation dates not established here

Evidence, location & limits

These studies cover different species, frequency ranges, devices, apps, protocols and outcomes; their results cannot be pooled into a general phone-quality score. The nightingale work is a Berlin project and its controlled device set was small. The bat work focuses on selected calls within 8–24 kHz at Italian and Spanish sites; it does not establish general ultrasonic capture above that range. Neither study validates current phones, current operating systems, browser capture or Talk2Nature's exact audio path. Acoustic recording or species-level detection does not demonstrate receiver comprehension or call meaning.

For Jäckel et al. (2021), reviewed the PLOS paper's methods, citizen-recording quality results, device playback comparison, discussion, rights and Zenodo data statement; the cited Zenodo deposit remains unreviewed. For Gili et al. (2024), reviewed the full publisher article and the March 13, 2024 Dryad record, visible file list and embedded README structure, as well as Dryad's CC0 and responsible-sharing policies. The separate README download returned HTTP 403. No audio archive, article supplement or code was acquired or reproduced, and no independent specialist review was completed.

This synthesis combines distinct field studies in Berlin, Northern Italy and Seville; no single point represents the evidence or validates global phone performance.

Location metadata reviewed: 2026-10-13. Map points are approximate locators, not sample coordinates.

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2022–2024Source year

Across animals / Tools & methods

Put the sound back into its scene ↗

Pose tracking and supervised behavior tools offer building blocks for connecting a call to visible movement.

No single mapped site

Observation dates not established here

Evidence, location & limits

Keypoints are estimates. Occlusion, identity swaps, unfamiliar species and camera angles require evaluation. Quadruped or mouse checkpoints cannot be assumed to work on parrots.

SuperAnimal abstract, introduction and dataset sections reviewed; SLEAP publisher summary and SimBA paper index and official project description reviewed. No code or model execution.

This note covers a tool, benchmark, method or claim review rather than one verified observation site.

Location metadata reviewed: 2026-10-03. Map points are approximate locators, not sample coordinates.

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2024Source year

Marine mammals / Acoustic analysis

Patterns inside sperm whale codas ↗

Analysis of sperm whale codas describes contextual and combinatorial acoustic structure.

Waters off Dominica

Recordings from 2005–2018

Evidence, location & limits

Conversational context and structural variation are not an English vocabulary or evidence of unrestricted translation.

Primary study summary in founding audit; full-text methods review pending.

The analysis uses the Dominica Sperm Whale Project’s Eastern Caribbean recordings.

  • Waters off Dominica · Study region

    The analysis uses the Dominica Sperm Whale Project’s Eastern Caribbean recordings.

    Location source ↗ · Results: The dataset

Observation-date source ↗ · Results: The dataset

Location metadata reviewed: 2026-10-03. Map points are approximate locators, not sample coordinates.

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2024Peer-reviewed dataset paper; author dataset linked at OSF

Great tit (Parus major) / Single-population wild song dataset

The Wytham great-tit dataset maps song structure, not meaning ↗

The Wytham Great Tit Song Dataset is an unusually dense wild-population resource: its authors report 109,963 annotated songs and 1,161,033 acoustic notes from more than 400 individual birds, collected in Wytham Woods, Oxfordshire, from 2020 to 2023. The complete current OSF API root lists 23 files totaling 12,184,678,510 bytes: 14 data files (12,184,652,639 bytes) and nine documentation files (25,871 bytes). The four song-archive parts total 11,380,411,540 bytes and ten metadata/map files total 804,241,099 bytes. The OSF node declares CC BY 4.0 and reports last modification on 2023-07-10; no explicit dataset version label was present in the inspected node response. The 2025 paper already trained a ViT metric-learning model on this corpus, generated 384-dimensional embeddings for all 109,963 songs, and used them to analyze repertoire similarity and demographic drivers of song culture. It then used automated clustering plus manual song categorization; the reported 0.87 validation accuracy belongs to a separate ResNet50 validation procedure. The dataset supports valuable song-structure and population-methods work, but it has no independently observed interaction context, receiver response or label for what a song means.

Wytham Woods region, United Kingdom

Observation dates not established here

Evidence, location & limits

This is one wild population and dawn-singing research system, so its labels do not establish transfer to other sites, species, household phones or general communication. Dataset documentation says bird identity associated with a breeding attempt can be uncertain or a proxy when the bird was not caught. The ordinary shared package is not the approximately 9 TB of original continuous soundscapes used in preparation. The authors’ nestbox metadata documentation specifies location coordinates in British National Grid and says the coordinates are also included in main.csv; the OSF manifest separately lists a Wytham perimeter shapefile. We inspected these documentation descriptions only, not coordinate values or file contents. Treat those fields as potentially sensitive wildlife-location information: keep public geography coarse and exclude precise coordinates from any future derivatives unless an appropriate wildlife-location review establishes a need and safe handling. The complete 2026-10-24 API tree has 23 entries; the earlier same-day 14-entry result was a partial traversal of data/ and omitted docs/. All 23 normalized paths and byte sizes match the historical snapshot. The 2026-10-23 local manifest stored a malformed 63-character SHA-256 for docs/metadata/main.md. On October 25, the current 6,426-byte file matched the OSF API SHA-256 and MD5; the cause of the malformed historical string is unknown. The file content was not reviewed, and no other provider hash was recomputed. Annotation values, grouping keys and dataset-level splits remain unaudited. The prior ViT embeddings were calculated across all songs from the same corpus, so treating them as a held-out-individual baseline for a new Talk2Nature benchmark would be an invalid shortcut; this is an evaluation-design inference from the published training scope. The 2025 paper already covers the obvious deep-embedding, individual-similarity and demographic song-culture tasks. No independent specialist assessed this note.

Reviewed the 2024 publisher article and author dataset documentation for collection design, labels and package format; Oxford Research Archive rights record; setup-code license and pinned revision; and the full 2025 Current Biology article including STAR Methods on feature extraction, similarity, clustering and validation. The 2025 methods describe a ViT metric-learning model trained on song-type labels from this corpus, 384-dimensional embeddings calculated for all 109,963 songs, and downstream repertoire-similarity/cultural analyses; the article reports 0.87 validation accuracy for a separate ResNet50 procedure on a random subset. The reviewed method text does not establish an individual-held-out test for that validation. The complete 2026-10-24 OSF root traversal lists 23 current file entries totaling 12,184,678,510 bytes: 14 data files and nine documentation files. An earlier same-day 14-entry traversal had listed only the data/ folder and omitted the separate docs/ folder; it is superseded. All 23 normalized paths and byte sizes match the 2026-10-23 snapshot. The saved 2026-10-23 SHA-256 for docs/metadata/main.md is malformed at 63 characters. On 2026-10-25, the 6,426-byte file was retrieved solely for integrity verification; its SHA-256 and MD5 match the current OSF API. Its content was not read or redistributed. The OSF node license relationship declares CC BY 4.0. The author nestbox documentation specifies British National Grid X/Y coordinates and says these data are also included in main.csv. No coordinate values, audio/annotation or analysis-code payloads were accessed; the one documentation file was not read or redistributed; no model was run and no independent specialist reviewed this Talk2Nature note.

Mapped to a coarse Wytham Woods study-region point from the published study-site description. The dataset documents nestbox coordinates; no coordinate values or data files were inspected or plotted.

  • Wytham Woods region, United Kingdom · Study region

    The primary article identifies its wild great-tit population at Wytham Woods, Oxford, UK.

    Location source ↗ · STAR Methods: Experimental model and study participant details; key resources table

Location metadata reviewed: 2026-10-24. Map points are approximate locators, not sample coordinates.

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2024Publication year

Yellow-naped amazon (Amazona auropalliata) / Longitudinal contact-call dialect study and data review

Amazon parrot dialects across 22 years: cultural change, not call meaning ↗

The paper's abstract reports that learned yellow-naped Amazon contact-call dialects shifted between 2005 and 2016: more sites used multiple call types, formerly distinct northern and southern regions became more acoustically similar, and new variants and greater acoustic variation appeared. The authors place these observations alongside a period of population decline. The version-6 Dryad deposit documents measurements and metadata for surveys in 1994, 2005 and 2016, but not the underlying audio.

Costa Rica

Contact-call surveys: 1994, 2005 and 2016

Evidence, location & limits

The reported temporal association does not show that population decline caused the vocal changes, nor that dialect changes altered receiver behavior, fitness or call meaning. This review is not full text: detailed statistical methods and supplements were not inspected. Dryad v6 lists 2,461 selected calls, 2,461 acoustic-measurement rows and 56 site-year rows; the selection table uses human visual/aural call-type labels, and BirdID is unique only within a site-year, not a stable cross-year identity. Raw audio and roost-count data are not in the deposit. The author README says exact site coordinates are withheld, while site names appear in the metadata; the atlas therefore uses only the existing country-scale Costa Rica locator. The code is MIT-licensed but its current reproducibility is limited by author-specific paths, unpinned dependencies and absent input files. File download failed with HTTP 401/403 in this environment; no bytes were acquired.

Reviewed the article abstract and targeted Methods/Results passages surfaced through the PMC search index; direct publisher retrieval returned HTTP 403 and the PMC page presented a bot check, so the full article and supplements were not reviewed. Reviewed the Dryad v6 landing-page data dictionary and rights statement, current Dryad reuse guide, TLS-verified API version/file manifest, and the GitHub README, license metadata, pinned tree and setup/input sections of four R Markdown analyses. No dataset bytes were acquired; no code or model was executed.

The article and dataset README place the study at multiple sites along Costa Rica's Pacific coast; the README says exact coordinates are not public. Reuse the existing country-scale Costa Rica point and omit site names and recording coordinates.

  • Costa Rica · Study region

    The primary dataset source identifies multiple Pacific-coast Costa Rica sites and explicitly says exact site coordinates are not made public. The public atlas uses only the existing country-scale point.

    Location source ↗ · Dryad dataset abstract and README; article abstract

Observation-date source ↗ · Dryad dataset abstract and README

Location metadata reviewed: 2026-10-14. Map points are approximate locators, not sample coordinates.

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2025Challenge year

Dogs / Audio benchmark challenge

Barkopedia: context labels do not show call meaning ↗

Barkopedia was an IJCAI 2025 challenge with eight dog-audio tasks: age, sex, breed, individual recognition, activity/environment, emotion, vocal separation and vocal detection. Its README describes the corpus as real-world clips sourced from YouTube and Reddit. The reviewed Activity and Environment card lists 12,480 training clips and 3,120 test clips, with eight activity categories and six environment categories. It says labels were initially generated through video-assisted inference with Janus-Pro-7B and later manually verified. This is a useful context-label classification comparator, but it does not show that an audio prediction represents an animal's intended message, subjective state or a validated welfare outcome. The pinned Hugging Face tree includes train, validation and test directories, although the card still says the test set will be released later. Its Dataset Viewer reports incompatible audiofolder/CSV formats across splits. The Barkopedia GitHub repository is the MIT-licensed challenge site/documentation; its inspected root tree did not include a training or evaluation implementation.

Location awaiting review

Observation dates not established here

Evidence, location & limits

The challenge timeline ended in 2025. No final leaderboard, peer-reviewed benchmark report, baseline implementation or seven other task-specific dataset cards were reviewed. The card does not specify per-recording source permissions, consent/privacy, annotator qualifications, blinded review, inter-annotator agreement, animal/session/site grouping or an untouched locked-test protocol. Its MIT metadata and the GitHub site's MIT LICENSE do not independently establish rights to source-platform recordings. The Viewer format error and card's future-tense test-set text show that the public metadata is not fully synchronized, but do not prove the audio files are unusable. No data, labels or sound were inspected, so no model performance or dataset quality conclusion is claimed. The former Barkopedia Task 6 Hugging Face card was later rewritten to describe EmotionalCanines and now links a separate dataset repository. Its README history does not establish that the historical challenge files and the EmotionalCanines release are the same data; do not treat the current card as a verified Task 6 dataset review.

Reviewed the official Barkopedia README and MIT LICENSE at GitHub commit ef8da9155e101cb76d4ee260bebb4d4fde4c2a1d; opened the linked task Spaces; reviewed the Activity and Environment dataset card and Hugging Face revision/tree at 60a721a7f9477634971f6b3df29dc2a5e56994d8. The README says source audio was collected from YouTube and Reddit and lists eight tasks. Only the Activity and Environment task card was examined; no academic challenge-results paper or final leaderboard report was reviewed. No archive, CSV, audio, code or model was downloaded or executed, and no independent reproduction or specialist review occurred. On October 14, also inspected the exact Hugging Face Task 6 card revision and its README diff: the former Barkopedia emotion card was rewritten as EmotionalCanines and changed its license metadata, but this does not establish dataset-file continuity. No Task 6 audio, labels or archives were inspected; the Activity and Environment card remains the only Barkopedia task-specific dataset card reviewed.

Location has not yet been checked in primary sources.

Location metadata reviewed: Not yet reviewed. Map points are approximate locators, not sample coordinates.

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2025Peer-reviewed article and Dryad dataset

Multiple mammals and birds; monk parakeet subset / Cross-species bioacoustic methods benchmark

Cross-species pitch benchmark helps estimate vocal pitch, not meaning ↗

The article reports 250,670 calls from 14 taxa with annotated fundamental-frequency (F0) contours. The Dryad release lists waveforms and annotations for 13 taxa; it also includes F0 predictions for bottlenose dolphins whose waveforms could not be published. The listed release totals 27.86 GB. Rodents contribute 224,705/250,670 calls (about 89.6%), while monk parakeets contribute 233. In the paper’s target-trained CREPE comparison, monk-parakeet pitch accuracy is 0.30 under that study’s half-semitone metric. This is an acoustic-feature estimation score, not a communication or meaning score. The 2025 paper cites Smith-Vidaurre et al. (2021, 2023) papers and deposits as sources for the 233 semi-automatically annotated monk-parakeet calls. The 2021 Figshare record lists 12 CSV/KML/README files and no waveform. The 2023 Figshare v2 inventory lists a 440,873-byte selection table and a 31,079,669-byte WAV archive; its README says their sound.file names correspond across those files and reports 1,596 calls in the table, 1,582 used in the paper. This offers a possible metadata-only source-file comparison, but the exact mapping of the F0 paper's 233 calls to the 2023 files remains unverified. The Dryad API v9 manifest lists 15 files totaling 27,863,357,661 bytes and identifies monk_parakeets.zip as 5,076,570 bytes with a provider-reported SHA-256. This is inventory metadata only: the archive and its file_list.txt have not been downloaded or inspected, and the checksum has not been recomputed locally.

Location awaiting review

Observation dates not established here

Evidence, location & limits

The benchmark targets a single F0 contour and discards overlapping sections; it does not label call category, behavioral context, addressee, receiver response or meaning. It is highly imbalanced by taxon, combines manual, semi-automatic and automatic annotation procedures, and model performance varies with signal characteristics. The 14-taxa article count and 13-taxa waveform deposit differ because dolphin predictions are present without the dolphin waveforms. The paper, data, code and weights have separate reuse terms. The exact source-recording provenance and independent-unit split structure were not reconciled in this review. The pinned 75-entry GitHub tree has no LICENSE/COPYING file; code and checkpoint terms remain unresolved. The 2025 article's main text does not give a call-to-file crosswalk. No archive or selection-table payload was downloaded, rows and KML contents were not inspected, and provider checksums were not recomputed locally. The 2023 README's file-name relationship is a lead, not a verified match to the F0 subset; source-recording provenance and location sensitivity remain open. The official Dryad file-download API returned an authorization requirement and the public file-stream route returned HTTP 403 in this environment; no archive member names or audio were inspected.

Reviewed the full 2025 PMC article, Dryad landing page and official dataset/version/file-manifest APIs, Dryad reuse guidance, and the pinned code tree. For source lineage, reviewed the 2025 paper's monk-parakeet table/references, the 2021 Animal Behaviour publisher abstract/data-accessibility statement and Figshare API manifest, plus the 2023 PLOS article record, Figshare v2 API and streamed README. The Dryad API snapshot is saved in research/dryad_f0_manifest_2026-10-24.json. No Dryad archive, audio, selection-table payload, code or weights were acquired or run; no Figshare rows or KML contents were inspected.

Location has not yet been checked in primary sources.

Location metadata reviewed: Not yet reviewed. Map points are approximate locators, not sample coordinates.

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2025arXiv v6, 2025; ICLR 2025 spotlight

Birds / Bioacoustic classification benchmark

BirdSet: a large bird-sound benchmark, not animal translation ↗

BirdSet is a large benchmark for recognizing bird species and related acoustic labels in recordings and soundscapes. The paper reports more than 6,800 recording hours, nearly 10,000 training classes and eight strongly labeled evaluation datasets totaling more than 400 hours. The live card lists a 528,434-recording XCL snapshot at 484 GB and recommends a regional HSN configuration with 5,460 training recordings, 10,296 test labels and 21 classes at 5.92 GB. These are upstream-reported benchmark figures, not a Talk2Nature reproduction. The task is acoustic classification; it does not test whether a receiver understood a call or what it meant.

Location awaiting review

Observation dates not established here

Evidence, location & limits

BirdSet is not parrot-specific and does not contain the receiver-response or validated behavior labels needed for a communication claim. Its card declares CC BY-NC 4.0 at the collection level and limits the dataset to non-commercial research and education, while individual Xeno-Canto recordings carry mixed terms; the maintainer thread records corrected metadata and a mix that includes CC BY, CC0, SA and NC-SA entries. The interaction between collection-level limits and individual rights remains unresolved, so no subset is admitted for Talk2Nature training or commercial use. The card itself is not revision-pinned, its compatibility notes conflict, and the live dataset viewer cannot load its custom script. Location fields are present and would need careful treatment.

Reviewed the primary arXiv abstract and version history, the current BirdSet dataset card and its configuration/metadata/license descriptions, and the complete maintainer clarification thread on mixed per-record licenses. No paper body, audio, labels, code or weights were acquired or run; no independent specialist reviewed this note.

Location has not yet been checked in primary sources.

Location metadata reviewed: Not yet reviewed. Map points are approximate locators, not sample coordinates.

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2025Source year

Marine mammals / Project announcement

DolphinGemma and shared signals ↗

DolphinGemma models patterns in dolphin sounds. The related CHAT work uses learned shared signals in human-dolphin interactions.

Bahamas · dolphin research waters

Observation dates not established here

Evidence, location & limits

Learning a shared interface and translating spontaneous natural communication are distinct scientific tasks.

Official announcement, including its field-program location paragraph, reviewed; not an independent replication or complete dataset audit.

A country-level locator for the field program described by the researchers. The model announcement does not provide a complete recording-date interval.

  • Bahamas · dolphin research waters · Study region

    The research team describes its long-term Atlantic spotted dolphin field program in the Bahamas.

    Location source ↗ · Researching dolphin society for decades

Location metadata reviewed: 2026-10-04. Map points are approximate locators, not sample coordinates.

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2025Dataset publication year

Birds, mammals, amphibians and insects / Passive acoustic dataset and benchmark

El Silencio helps detect sound across taxa, not decode meaning ↗

El Silencio is a useful multi-taxon sound-event detection candidate: the paper describes 770 one-minute soundscapes (12.8 hours) and about 15,000 event boxes across birds, mammals, amphibians and insects. The manual protocol labels acoustic group, call/sonotype and sound form; it groups same-species signals separated by no more than one second. These acoustic/taxonomic labels do not supply independently observed behavior, caller intent or receiver-response labels. The paper's BirdCLEF+ competition dataset is a separate sound-identification task, so its classification score is not a result on this El Silencio subset and does not measure communication. This can help test a multi-species detection/data workflow, but it is not a communication or meaning benchmark.

Colombia (country-scale reference)

Dataset README describes dawn/dusk labeled recordings from March and August 2023

Evidence, location & limits

The paper's unlabeled snapshot is 11,340 recordings at 63 sites; the later pinned README and separate Zenodo deposit describe 10,980 recordings at 61 sites (360 fewer across two sites), with no explanation in reviewed sources. The protocol's explicit group codes are birds, anurans and insects, while the paper describes four taxa including mammals; the mapping needs specialist clarification. No inter-annotator reliability statistic was reported in the reviewed paper/protocol. The labeled Zenodo page displays 109.8 GB while its nine-file API inventory totals 3,546,609,904 bytes; unresolved. The unlabeled API inventory lists 61 ZIPs totaling 49,620,235,222 bytes. Zenodo declares CC BY 4.0 at record level; source-media rights, consent/privacy, human-audio handling and sensitive-location conditions remain unreviewed. UNKNOWN is not a stable class. No audio, annotations or split were inspected, and no baseline was run. Event detection, acoustic association and communication meaning remain different claims.

Queried official Zenodo API metadata for labeled record 17064353 and unlabeled record 17122738, including publication dates, record-level licenses, file counts, byte totals and provider MD5 values. Read the dataset repository README and MIT LICENSE at commit 5e2f7826c908fec3018740068bbdeda6c6977f35; reviewed all extractable Spanish text on pages 2-18 of its pinned protocol and the full 11-page CEUR overview paper. Local review copies of only the paper and protocol are excluded from the public site. No dataset audio, label table, code or model weights were acquired or run; no baseline was reproduced.

The primary dataset record locates its seven-site collection only to the Middle Magdalena region of Colombia. The public map uses a country-scale Colombia reference only; it does not show the region centroid, any of the seven sites, recorder positions or animal locations.

  • Colombia (country-scale reference) · Study region

    The primary dataset record supports the broad Colombia study region, not a point within it. The displayed GeoNames country reference is rounded to one decimal and is explicitly country-scale; it is not the Middle Magdalena centroid, a site, recorder or animal coordinate. No site-level coordinates, rows or media were reviewed.

    Location source ↗ · Zenodo record 17064353, v1 description: Middle Magdalena region, Colombia; seven recording sites

Observation-date source ↗ · Zenodo description and linked repository README

Location metadata reviewed: 2026-10-25. Map points are approximate locators, not sample coordinates.

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2025Publication year

Domestic dog (Canis familiaris), Siberian Husky and Shiba Inu / Dataset and audio-classification study

EmotionalCanines: video-derived labels are not a dog-reported emotion ↗

The paper predicts two three-class labels from audio: arousal (low/medium/high) and valence (negative/neutral/positive). Annotators first watched the associated video with surrounding context and used visible behavior and situation to assign the labels; samples were retained only when at least two of three raters agreed on both dimensions. These are human video-derived behavior/affect annotations, not an animal-reported state, an independently validated physiological measure, a receiver response, or evidence that a bark means a particular thing. The article reports dog-separated tests with best accuracies around 50–57% across breed and target combinations. It separately reports higher 10-fold cross-validation results, but the reviewed methods do not specify whether those folds were grouped by dog. The Hugging Face path formerly used for Barkopedia Task 6 was rewritten at the pinned revision as EmotionalCanines and its license metadata changed; no continuity of the underlying files was verified.

Location awaiting review

Observation dates not established here

Evidence, location & limits

The paper says its source pipeline began from internet video-derived bark sequences and acknowledges engagement-related sampling bias. The emotion labels depend on human interpretation of video behavior/context; the 100-sample pilot reports fair agreement, not external ground truth, and the authors discuss the difficulty of directly measuring subjective state from visible behavior. The dataset covers two breeds and only 35 minutes of bark sequences; each breed has 100 held-out test sequences. The reported 10-fold CV grouping is not clear from the reviewed methods, and no baseline code or results were independently reproduced. The dataset repositories declare CC BY-NC-SA 4.0, but permissions, consent and privacy terms for original source videos were not independently traced. Do not treat audio classification as emotion diagnosis, welfare advice or communication translation.

Read the author-hosted eight-page ACM Multimedia paper sections on dataset generation, annotation, reliability, dog-separated splits, baselines, results and limitations. Reviewed the exact Hugging Face card commit c0617b15562882b9eee4cc1bcb8e6aa80e51bd34 and its README diff, plus the pinned GitHub README/LICENSE at 6b73c5fce55fc132e97db6a76899f8594a955f59. The paper reports 1,400 bark sequences (700 per breed; 600 train and 100 test per breed) from 199 dogs, with dog identities separated between train and test. Twelve annotators contributed; each sequence received three labels, and a 100-sample pilot reported Fleiss kappa 0.30 for arousal and 0.42 for valence. No recordings, labels, code or model weights were downloaded or run; no metrics were reproduced.

Location has not yet been checked in primary sources.

Location metadata reviewed: Not yet reviewed. Map points are approximate locators, not sample coordinates.

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2025Publication year

Fusarium oxysporum (filamentous fungus) / Extracellular electrophysiology and stimulus-response study

Fungal electrical activity: a sensor study, not a decoded language ↗

Buffi et al. tested a printed fungal-potential card on one Fusarium oxysporum strain (NEU 195). Table 1 reports a significant pre-induction power-spectral-density difference of about 1,604.752% between 24 fungal electrode-pair records and 8 non-inoculated control records (p<0.001). The study supports a specialized laboratory measurement of fungal-associated electrical activity; it does not decode a fungal language.

Location awaiting review

Observation dates not established here

Evidence, location & limits

This is a laboratory sensor/method study on one strain, not an animal-style communication experiment. Extracellular voltage and frequency patterns do not identify a message, receiver, referent or semantic code. For the post-induction drug/carrier comparisons, Table 1 lists n=3 and no contrast is statistically significant: cycloheximide −74.214% (p=.327), voriconazole +1,201.803% (p=.391), calcimycin +58.503% (p=.580), and sodium azide +1,358.933% (p=.116). The calcimycin pattern matched its DMSO carrier, which the authors consider likely electrode interference; the sodium-azide series also had a brief water-control anomaly. The main text does not establish whether electrode-pair counts are independent biological replicates. The 2024 critique and 2025 review describe unresolved measurement and interpretation challenges. Mendeley displays CC BY 4.0 but its visible Files section contains no file entries; no dataset files were reviewed. The article's data statement points to Method S1 for code, while its software table points to Document S2; the supplement, code bytes and component-specific terms remain unreviewed.

Reviewed the full PMC article text, including Methods, Results and Table 1, figures, Discussion, data statement and article license, plus the Mendeley Data v1 description, displayed license and visible Files section. Table 1 reports electrode-pair counts, but the reviewed main text does not establish their independent culture-level replication. Supplementary Document S1 (including Method S1), raw dataset files and code bytes were not inspected or executed; no result was reproduced.

Location has not yet been checked in primary sources.

Location metadata reviewed: Not yet reviewed. Map points are approximate locators, not sample coordinates.

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2025Source year

Plants & fungi / Methods & critique

Fungal electricity needs careful controls ↗

Measurement work and critiques highlight difficulties in separating fungal electrical activity from environmental artifacts.

Location awaiting review

Observation dates not established here

Evidence, location & limits

A detected pattern or analogy to words is not evidence of a semantic language.

Methods discussion and critique reviewed for the audit; no independent replication performed.

Location has not yet been checked in primary sources.

Location metadata reviewed: Not yet reviewed. Map points are approximate locators, not sample coordinates.

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2021–2025Source year

Plants & fungi / Measurement, hypothesis & critique

Do fungi have a language? What electrical recordings show ↗

Adamatzky's peer-reviewed 2022 paper reports extracellular voltage recordings from four fungal species. It sampled one value per second from differential electrode pairs placed 1–2 cm apart, recorded up to eight pairs at once, and reports about 1.5 days for Schizophyllum commune and about five days for each other species. The analysis used species-specific thresholds to identify spikes, then grouped them into proposed “words” using one or two times each species' mean inter-spike interval. The resulting spike-count distribution was compared with human-language word lengths. This is a published pattern-analysis hypothesis, not a decoded fungal vocabulary or conversation.

Location awaiting review

Observation dates not established here

Evidence, location & limits

The grouping threshold is an analyst-defined analogy, and similar word-length distributions do not identify a sender's message, a receiver, a referent or a semantic code. The 2024 Blatt et al. commentary argues that the language claim is premature and that non-biological voltage noise and artifacts may account for measured patterns; its full text was not accessible in this review. The 2025 FEMS review describes an attempted method replication in Pleurotus pulmonarius: it reports disturbance-associated spikes from opening the incubator, nearby footsteps and automatic window shades in both fungal and control recordings, and calls for stronger environmental shielding and measurement controls. That is a methods warning, not a definitive disproof of all fungal electrical physiology. The linked 2021 Zenodo dataset lists four archives totaling 84.6 MB, but no license was displayed in its Rights section. No archive, code or weights were acquired, and no benchmark or model was run.

Reviewed the full arXiv HTML text of Adamatzky's 2021 preprint, including recording methods, event detection, grouping rules, results and discussion; checked the published 2022 Royal Society article metadata and institutional article-rights record. Reviewed the 2024 critique's publisher abstract and visible selected sections, not its full text. Reviewed the full 2025 FEMS review sections on fungal electrophysiology, macroscopic mushroom recordings, the authors' method-replication attempt, caveats and future methods, plus its article license. Inspected the official Zenodo v1 record and file metadata only. No files were downloaded, no analysis was reproduced, and no independent specialist review was received.

Location has not yet been checked in primary sources.

Location metadata reviewed: Not yet reviewed. Map points are approximate locators, not sample coordinates.

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2025Publication year

Birds / Playback experiment

How great tits respond to a distant species’ mobbing calls ↗

Great tits approached the loudspeaker more often during call playbacks than during control playbacks, but approach did not significantly differ among call treatments. Wing-flicking was more likely after the complete two-part call sequence (labelled FME–D by the authors) than after D-only calls or controls for both great-tit and Carolina-wren stimuli. The study reports no significant difference between the two species’ sequence treatments (P=0.12; wide confidence interval), so it does not establish their equivalence.

Lyon region, France

16 playback days in September 2022

Evidence, location & limits

This is one field playback study of European great tits responding to unfamiliar Carolina wren calls, not a test of arbitrary call combinations or open-ended communication. The authors note that the alert/recruitment functions of the Carolina wren's FME and D parts were not confirmed. It did not include reversed-order Carolina-wren sequences, so it does not isolate call order as the cause. Carolina-wren stimuli came from four recorded individuals but were reduced to two constructed tracks per call treatment; the authors note that more stimulus diversity would be ideal. Similar behavior can reflect response to a mobbing-like acoustic pattern without demonstrating a shared semantic representation or receiver translation. No Talk2Nature model or app feature was tested.

Reviewed the publisher-indexed Methods, Results and Discussion passages and the University of Western Australia repository abstract and publication/license metadata. The publisher states 96 wild great tits, six playback treatments with 16 birds each, and a single playback per bird. The indexed text exposes stimulus construction and behavioral outcomes; direct ScienceDirect retrieval returned HTTP 403, so figures, supplements, full statistical details, and article-level context were not reviewed.

Publisher-indexed Methods place the playback work in mixed forests near Lyon; the public marker is rounded to a broad region and does not identify playback sites.

  • Lyon region, France · Study region

    The source reports mixed forests near Lyon, France; the map uses an editorially rounded regional locator.

    Location source ↗ · Methods: Study Site and Field Procedure

Observation-date source ↗ · Methods: Study Site and Field Procedure

Location metadata reviewed: 2026-10-13. Map points are approximate locators, not sample coordinates.

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2025Paper and dataset release year

Mammals / Multimodal camera-trap benchmark

MammAlps aligns animal sound with video-annotated behavior ↗

MammAlps provides a real multimodal wildlife behavior benchmark: animal-centered video clips have matched audio and scene maps, with visible behavior labels assigned by two annotators. It evaluates observable actions and activities, not vocal meaning or animal translation.

Swiss National Park region, Switzerland

Observation dates not established here

Evidence, location & limits

Five wild mammal species were recorded at three sites in the Swiss National Park over six weeks; the authors withhold the exact site locations. Collection dates conflict across versions: the arXiv v2 and official CVPR main text say June–August 2023, while the arXiv/CVPR supplementary section 7.1/A.1 and Zenodo v3 say August–October 2023. The conflict persists in the published version; direct CVF fetches returned 403 and only indexed excerpts were reviewed. The period is unresolved and is not used in the atlas timeline. The published split holds out days, not verified animal identities; cross-day individual independence is unestablished. Reviewed passages describe two annotators and an unknown class but do not establish inter-annotator agreement statistics or that labeling was audio-blind. The authors acknowledge subjective behavior categories and geographic/time limits. Version 3's full data archive is 87.9 GB. Dataset terms are CC BY-NC 4.0, which excludes Talk2Nature's commercial-capable benchmark path. No audio alignment, labels, model weights, preprocessing or published result were independently reproduced.

Targeted review of arXiv v2 sections 3.1–3.4, 5, 7.1 and 9.1–9.2; exact Zenodo v3 metadata, README, file inventory and dataset license; official CVPR Open Access indexed excerpts from the main paper and supplementary section A.1; official GitHub README, evaluation interface and LICENSE.md. The direct CVF paper and supplement fetch returned 403, so only targeted indexed excerpts were reviewed for the published version. Other supplementary methods and archive contents were not reviewed; no data or code were downloaded or run.

The study names three sites in the Swiss National Park but withholds their exact locations. The map shows only an approximate park-region marker. The arXiv and CVPR main text, supplement, and Zenodo metadata conflict on collection months; the conflict persists in the indexed CVPR excerpts, so no observation period is plotted pending clarification.

  • Swiss National Park region, Switzerland · Study region

    The methods place three camera-trap sites in the Swiss National Park and explicitly withhold their exact locations. Only a coarse regional point is shown.

    Location source ↗ · arXiv v2, section 3.1 and Supplementary section 7.1

Location metadata reviewed: 2026-10-11. Map points are approximate locators, not sample coordinates.

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2025Published May 7,

Parrots / Vocal variation and social networks

Parrot social ties track call variation, not call meaning ↗

This study combines social-network observations with monk-parakeet vocalizations. It reports networks for 337 birds, relatedness for 100, and 5,599 vocalizations from 229 individuals over two years. Call-diversity associations varied by year; close affiliates had more dissimilar calls. These are observational associations, not evidence that a specific call carries a known message.

Barcelona region, Spain

27 October–19 November 2020 and 31 October–30 November 2021; 55 days total

Evidence, location & limits

The exact 5,599-call and 229-bird totals match the 2023 monk-parakeet voiceprint paper, so this is a new analysis of an overlapping cohort, not an independent acoustic replication. The study does not test receiver responses or experimentally established meanings. The authors discuss active differentiation as an interpretation; the reported associations alone do not establish that cause. The Zenodo archive was not inspected, and the GitHub README says some required inputs are not shared. Its documented nest/UTM location fields make location review necessary before any mapping. No coordinates are reproduced here.

Reviewed the published article abstract and targeted methods, results, discussion, data-access, ethics and rights passages using the primary full-text record and institutional copy; checked the 2023 voiceprint paper for sample overlap; reviewed the Zenodo record and the GitHub README/tree at a pinned revision. On October 24, rechecked Methods section 2.1 and confirmed the Barcelona study region and field dates for the atlas. The archive, raw recordings, code and model outputs were not acquired or run.

The public atlas places this study at the existing coarse Barcelona region and does not plot a park, nest or UTM coordinate. The article names a field site; archived spatial fields were not inspected.

  • Barcelona region, Spain · Study region

    Methods describe fieldwork in Barcelona, Spain, during the 2020 and 2021 field seasons; the atlas uses a coarse city-region point.

    Location source ↗ · Methods section 2.1, Study system

Observation-date source ↗ · Methods section 2.1, Study system

Location metadata reviewed: 2026-10-24. Map points are approximate locators, not sample coordinates.

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2025Source year

Machine learning / Software documentation

Perch: reusable representations of animal sound ↗

Perch v2 provides acoustic embeddings for downstream classifiers. The official CPU version 1 card declares Apache-2.0 and says its weights match the GPU perch_v2 version 2. Pinned Hoplite configuration uses five-second, mono 32 kHz windows and a 1,536-dimensional embedding.

No single mapped site

Observation dates not established here

Evidence, location & limits

Google's card names Xeno-Canto, iNaturalist, Animal Sound Archive and FSD50k as training sources, but the item-level training data and license provenance, target-data overlap and scientific fit remain unverified. The card cautions that logits are uncalibrated and may be unreliable for rare species. No bundle checksum, runtime, accuracy or mobile performance was verified.

Official parent card and exact perch_v2_cpu/1 variation page reviewed, along with the repository migration notice, Hoplite README and pinned model configuration. No weights downloaded and no inference run.

This note covers a tool, benchmark, method or claim review rather than one verified observation site.

Location metadata reviewed: 2026-10-14. Map points are approximate locators, not sample coordinates.

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2023–2025Peer-reviewed debate and response

Plants & fungi / Evidence review and debate

Can trees talk through fungi? What mycorrhizal networks show ↗

A common mycorrhizal network describes fungal hyphae shared by roots of two or more plants; such connections have been documented in particular systems, while their prevalence and functions remain debated. Whether material moves through them, whether that movement changes a receiver, and whether information is communicated are separate questions. Karst, Jones and Hoeksema (2023) warn that popular and scientific claims about widespread forest networks and beneficial “mother tree” transfers can outrun the evidence and report positive citation bias. Henriksson et al. (2023) argue that isotope studies do not yet establish significant net carbon transfer through these networks that benefits recipients, and discuss alternative pathways. Simard, Ryan and Perry (2025) respond that multiple methods support network connections and context-dependent transfer in forests. This published disagreement is a reason to state the evidence level precisely, not to declare either that networks never occur or that trees have a demonstrated shared language.

Location awaiting review

Observation dates not established here

Evidence, location & limits

This debate is especially about forest ecology and ectomycorrhizal systems; it does not settle every plant–fungus relationship. A fungal connection or isotope transfer is not by itself evidence that one plant sent information another received. The 2025 response is explicitly an Opinion by researchers involved in this field, not an independent replication. One author of the 2023 New Phytologist review disclosed a financial interest in an organic-fertilizer company. The linked primary studies and their methods were not exhaustively reviewed here.

Reviewed the Nature publisher record and surfaced article passages plus its author correction; the New Phytologist summary and selected discussion; and the full publisher-hosted Frontiers Opinion, including its article type and author statements. Direct rendering of the Nature article required publisher authentication in this review. The underlying studies, supplementary files and data were not reanalyzed, and no specialist review was obtained.

Location has not yet been checked in primary sources.

Location metadata reviewed: Not yet reviewed. Map points are approximate locators, not sample coordinates.

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2025arXiv v1 posted 2025-12-01; official project repository identifies the work as NeurIPS 2025

Sperm whale · cetacean bioacoustics / Generative audio and representation learning

WhAM: acoustic style transfer is not whale translation ↗

WhAM fine-tunes the music-pretrained VampNet masked acoustic token model in two stages: domain adaptation on animal-audio collections, then sperm-whale-specific training on DSWP and CETI. The paper describes 2,507 DSWP and 7,653 CETI annotated codas for this work. It evaluates generated audio with Fréchet Audio Distance and a five-expert listening study, and tests learned embeddings on coda detection, rhythm, social-unit and vowel-type classification. The paper explicitly defines “translation” as converting prompts, including noise or other animal sounds, into sperm-whale coda-like acoustic style; it says semantic translation remains a separate, more ambitious goal. On the reported classification table, WhAM exceeds simple random/majority baselines but the reported specialist or general bioacoustic embeddings perform better across these tasks. For example, social-unit accuracy is 70.5% for WhAM versus 93.2% for BirdNET. The paper describes social-unit classification within data from a single vocal clan as closer to speaker identification than dialect classification.

No single mapped site

Observation dates not established here

Evidence, location & limits

The downstream protocol describes an 80/20 label-stratified train/test split and selects the best classifier checkpoint based on test-set performance. The reviewed description does not establish a separate validation set or grouping by whale, recording, session or source, so the reported test metrics should not be treated as an untouched independent estimate. The public DSWP card currently lists 1,501 isolated snippets (~45 minutes; 585 MB), fewer than the paper’s 2,507 DSWP training codas; the exact relationship is unresolved. The card says the clips lack behavioral context and per-file recording-system metadata and all come from one clan and region. The paper identifies codec limitations, unnatural synthetic click onset/decay/background, echolocation-like artifacts and possible echolocation contamination in data prepared as communication codas. Its expert study involved five specialists with only moderate inter-expert agreement. The acoustic outputs and classification labels do not demonstrate receiver comprehension, behavioral function or meaning. The paper is CC BY-NC-ND 4.0, code MIT, DSWP card CC BY 4.0, while the weight record displays no license; these are distinct component rights. No independent reproduction or specialist review occurred.

Read the full arXiv v1 main text from abstract through limitations, and reviewed targeted Appendix E sections on training data, compute, downstream classification and expert perceptual evaluation. Checked the official repository README/license and pinned source revision, the DSWP dataset card and its current repository revision, and the Zenodo model-weight record. No code, audio, datasets, model weights, paper figures or supplementary files were downloaded or run; Appendix D and artifact contents were not fully audited.

This is a model and dataset review, not a new observation or mapped study-site record.

Location metadata reviewed: 2026-10-13. Map points are approximate locators, not sample coordinates.

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2025Methods in Ecology and Evolution 16:19–28; version of record online 2024

Cross-species bioacoustics / Annotation software

Whombat: learn from an existing bioacoustic annotation workbench ↗

Whombat is a browser-based workbench for managing bioacoustic projects, reviewing recordings and human annotations, and visualizing or exchanging machine-learning predictions. The paper describes bat and avian applications. Its guide imports an AOEF dataset with an audio directory first, then its annotation project; evaluation sets and model runs are separate, and predictions are distinct from human annotations. Talk2Nature now offers an optional experimental one-way AOEF 1.1 package. The exact export imported into Whombat 0.9.0 with one recording, intervals [1, 2] and [4, 5] seconds, four visible namespaced project tags, and two notes. A missing AOEF project_tags list and a macOS canonical-path issue were identified and fixed/documented during this synthetic compatibility test.

No single mapped site

Observation dates not established here

Evidence, location & limits

This is annotation and model-assisted review infrastructure, not a decoder of animal intent or a general animal-language translator. The app-level check establishes format interoperability only for Whombat 0.9.0 and the recorded test stack; other dependency versions, mobile behavior, real recordings, label quality and research suitability remain unverified. The test used invented audio only. Full Talk2Nature IDs, declared animal context and any Station journal stay in an unencrypted provenance sidecar; inspect it before sharing. Whombat's path check requires one canonical absolute audio-root path; on macOS /tmp may resolve to /private/tmp. No reverse import exists, and the sidecar is still the source of truth. The Whombat v0.9.0 code is GPL v3; the soundevent library is MIT; the article is CC BY 4.0. These licenses cover separate assets.

Reviewed the complete primary article through the publisher/UCL open-access record and the pinned Whombat README/license and PyPI release metadata. Read the official import, dataset, annotation-project, evaluation and installation guides. Verified the temporary Whombat 0.9.0 wheel SHA-256 against PyPI and inspected the separate Whombat GPL v3 and soundevent MIT licenses. In an isolated Python 3.12.5 environment on macOS 26.4, the exact current Listen export passed Whombat's dataset-then-project UI import. The app created one recording, two TimeInterval annotations, two notes, one task and four project tags; checksum, sample rate and duration matched, the tag table showed both sound events, and both imported creator IDs remained null. Core resolved versions were FastAPI 0.112.2, fastapi-users 12.1.3, Starlette 0.38.6 and SQLAlchemy 2.0.54. The test used only an invented WAV and a temporary SQLite database; the wheel, environment and test outputs were removed. No animal audio, participant record or model weight was tested.

This note reviews annotation software and published use cases, not a location-specific observation study.

Location metadata reviewed: 2026-10-13. Map points are approximate locators, not sample coordinates.

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2025Peer-reviewed system paper; related dataset records span 2024–2026

Zebra finch (Taeniopygia guttata) / Multimodal bioacoustic methods and datasets

Synchronized finch recordings help locate calls, not translate them ↗

BirdPark is a specialized synchronized recording system combining video, stationary microphones and animal-borne accelerometer transmitters. The paper uses redundant channels to help find vocal events and separate likely caller sources in freely behaving zebra-finch groups. The public records describe several related but distinct releases: a Zenodo v2 record claiming 112 minutes across four experiments, a later annotator-training pack with a 4.5 GB pair-example archive, and an ETH record describing ten mixed-sex pairs (20 birds) with annotated clean and overlapping vocalizations. These resources could inform bounded vocal-event, overlap and caller-attribution methods. Their labels do not establish what a call means or whether a bird understood a message.

Location awaiting review

Observation dates not established here

Evidence, location & limits

The article's searched Table 2 passage describes a 28-minute segmentation evaluation slice, while Zenodo v2 describes 112 minutes across four experiments; the relationship and exact common sample coverage were not reconciled, so these counts must not be combined. The Zenodo records show no dataset license field; the ETH record says In Copyright - Non-Commercial Use Permitted, which does not clear commercial training or redistribution. The separate releases may overlap and are not independent cohorts by default. The recording setup is a specialized lab system with microphones, cameras and body-worn sensors, not an ordinary phone workflow. The inspected labels concern event boundaries, signal visibility or source attribution, not independently established call meaning, receiver response or open-ended communication. Split membership, annotator agreement and component provenance remain unreviewed.

Reviewed the PubMed bibliographic record and abstract, searchable PMC passages on shared-clock synchronization, multimodal vocal segmentation and channel-miss evaluation, the ETH Research Collection article-rights record, and exact Zenodo v2 descriptions and file inventories for records 13144875 and 20608098. The ETH Zurich dataset record was reviewed for its description and rights label. The complete paper, supplement, annotation guides, CSV contents, audio/video/accelerometer files, code and model weights were not inspected or acquired. No independent avian or ML/statistical reviewer assessed this note.

Location has not yet been checked in primary sources.

Location metadata reviewed: Not yet reviewed. Map points are approximate locators, not sample coordinates.

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2025Peer-reviewed Science paper

Zebra finch (Taeniopygia guttata) / Call-category perception study

Zebra finches categorize call types; translation remains a bigger question ↗

The paper reports that 12 adult zebra finches learned to discriminate all 11 researcher-defined call types in an operant Go/No-Go task. The stimuli came from a library described as more than 8,000 call renditions by 45 vocalizers, and birds generalized to unfamiliar renditions. The paper already compares acoustic-classifier confusion with behavioral confusion and reports more errors among call types used in similar behavioral contexts than its acoustic-similarity comparison predicted. The authors' README describes the spectrogram classifier matrices as leave-one-vocalizer-out LDA results. Thus basic model-versus-bird confusion alignment is published prior art. A separate 2026 article applies BirdAVES to supplied call labels, but the reviewed article does not directly compare those modern-model errors with the measured behavioral confusion matrix.

Location awaiting review

Observation dates not established here

Evidence, location & limits

The task was trained, closed-set discrimination of human-defined call categories, not spontaneous conversation, receiver behavior in a natural exchange or open-ended translation. The behavioral and acoustic confusion matrices are described at call-category level, so a matrix-level reproduction may not require a one-to-one audio-to-trial join if the exact published matrices are present in a rights-cleared release. A new raw-audio representation comparison does require matching audio files to call labels, vocalizer identities and the same stimulus population. The Figshare record lists 3,433 WAV files, some containing sequences; their mapping to the paper's more than 8,000 renditions and to the 2026 classifier inputs is unverified. The final Science paper links the versioned v1 record 13623969; the Berkeley copy cites concept DOI 13623968, whose later v3 release is record 15795568. This citation resolution does not establish which archive files support the final published analyses; archive contents and component-level rights remain unresolved. Figshare and Zenodo declare CC BY 4.0 at record level, while the GitHub repository displays no license file. The Figshare landing-page description gives the filename pattern ColColxxxx_yymmdd-VT-zz.wav, which appears to encode vocalizer, date, vocalization type and rendition, but it warns that naming discrepancies are known. Its current API manifest lists two ZIP files and a notes PDF, without per-file MD5 values; the page text says the archives contain 3,433 WAVs. These are possible join keys, not a verified mapping to the 2025 stimulus set. Zenodo's current v1 and v3 API records report provider MD5 values, but archive contents were not retrieved and those checksums were not verified locally.

Reviewed the full PMC-indexed article text, including the abstract, reported results, Figure 4 caption and data statement on October 24; the UC Berkeley institutional-copy record; the authors' public GitHub README/root listing; and the 2026 BirdAVES article. On October 25, refreshed official Figshare v1 and Zenodo v1/v3 API metadata over verified TLS, including current file inventories, provider checksums and record-level license declarations. The Figshare landing-page description exposes a filename convention and warns of known naming discrepancies. No Figshare or Zenodo payload, article supplement, audio, source code or model weights were downloaded or reproduced; the new publisher search extract was used only to confirm the 2026 article's title, framing and reported result excerpt.

Location has not yet been checked in primary sources.

Location metadata reviewed: Not yet reviewed. Map points are approximate locators, not sample coordinates.

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2026Trends in Cognitive Sciences, online ahead of print 22 July 2026

Multiple taxa; the review discusses spectacled parrotlets, common marmosets, African elephants and bottlenose dolphins / Conceptual review

Do animal calls function as names? A proposed test framework ↗

Jaakkola, King and Bruck propose three minimum criteria for identifying animal signals as names: individual reference, symbolic representation and shared meaning. Their abstract says the review considers evidence about name-like calls in spectacled parrotlets, common marmosets, African elephants and bottlenose dolphins; publisher highlights say much of the presented evidence is equivocal. This is a conceptual review, not a new experiment or an ML result.

Location awaiting review

Observation dates not established here

Evidence, location & limits

The PubMed abstract and ScienceDirect publisher highlights/metadata were available; the direct full-text page returned HTTP 403. The operational definitions, evidence grading, counterexamples and detailed treatment of each species therefore remain unreviewed. The criteria are the authors' proposal, not a demonstrated universal consensus. A signal associated with an individual or followed by a response does not by itself establish symbolic reference or shared meaning.

Reviewed the official PubMed record and ScienceDirect listing for article metadata, abstract, publisher highlights, Open Access/Creative Commons status and copyright information. The direct publisher page returned HTTP 403. Full text, references, supplementary files and underlying studies were not reviewed; no data or analyses were acquired or reproduced.

Location has not yet been checked in primary sources.

Location metadata reviewed: Not yet reviewed. Map points are approximate locators, not sample coordinates.

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2024–2026Source year

Mammals / Model and dataset

animal2vec and MeerKAT: rare-call detection, not translation ↗

The peer-reviewed study presents animal2vec as a rare-event bioacoustic detector/classifier and MeerKAT as 1,068 hours of 8 kHz meerkat audio, including 184 hours with complete event labels across 66,398 ten-second samples. The paper reports eight vocalization classes and three non-call event classes, overlapping labels, millisecond event boundaries and inter-annotator timing reliability just over 10 ms. It is a valuable example of continuous audio, quiet/background intervals and event-based precision–recall evaluation.

Northern Cape, South Africa

Field seasons: Aug–Sep 2017 and Jul–Aug 2019

Evidence, location & limits

The data combine animal-worn collars and close directional microphones, not ordinary phone recordings. The paper describes a stratified five-fold split over samples but this review did not verify that folds separate animals, days or source recordings. Public filenames are randomized and identity/time order are intentionally unavailable; the authors say these can be requested. A separate held-out-data analysis is not evidence of unseen-animal generalization. The focal label is an analyst's source attribution based partly on relative intensity, spectral change and context; it is not meaning. Predicting assigned call categories, including labels such as alarm or social call, does not demonstrate receiver comprehension or translation. Dataset and checkpoints are CC BY-NC 4.0; the code is MIT. Those rights are separate, and the data/weights are outside the project's commercial-capable path. No media, code or weights were acquired or run.

Reviewed the 2026 published article's dataset methods, class definitions, annotation quality, evaluation design/results, discussion, ethics and data-availability statement; the official animal2vec repository and README; and the article's separate dataset, code and checkpoint license statements. The article supplement, deposited files/manifests, archived code contents and model weights were not inspected or acquired. No published result was reproduced.

The paper locates collection at the Kalahari Research Centre in South Africa. This is a regional locator, not a tracking location.

  • Northern Cape, South Africa · Study region

    The paper locates collection at the Kalahari Research Centre in South Africa. This is a regional locator, not a tracking location.

    Location source ↗ · Section 2.1: The MeerKAT bioacoustic dataset

Observation-date source ↗ · Section 2.1: data collection

Location metadata reviewed: 2026-10-03. Map points are approximate locators, not sample coordinates.

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2026Documentation checked

Research tools / Software documentation

Start with tools researchers already use ↗

Arbimon, BORIS and BirdNET review tools already cover parts of acoustic analysis, behavioral annotation and detection review.

No single mapped site

Observation dates not established here

Evidence, location & limits

Feature availability does not prove an unmet product need or interchangeability between annotation formats.

Official tool documentation reviewed for the audit.

This note covers a tool, benchmark, method or claim review rather than one verified observation site.

Location metadata reviewed: 2026-10-03. Map points are approximate locators, not sample coordinates.

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2022–2026Source year

Cross-species / Model and code

AVES and AVEX: reuse an encoder before training one ↗

Self-supervised audio encoders learn from unlabeled recordings. AVEX provides a common interface for extracting features and fitting small supervised classifiers.

No single mapped site

Observation dates not established here

Evidence, location & limits

Useful features or benchmark scores do not establish call meaning. Pretraining overlap, sample-rate choices and checkpoint rights need checking for the exact experiment.

AVES abstract and official AVES/AVEX READMEs, including embedding examples and the AVEX license declaration. No model execution or checkpoint audit.

This note covers a tool, benchmark, method or claim review rather than one verified observation site.

Location metadata reviewed: 2026-10-03. Map points are approximate locators, not sample coordinates.

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2026Publication year

Seba’s short-tailed bat / Neurophysiology study

Bat auditory cortex anticipates call patterns, not their meaning ↗

In ten awake Seba’s short-tailed bats, the authors report auditory-cortex firing patterns associated with whether the animal produced an echolocation pulse or a social communication call, and whether some calls occurred singly or in short sequences. Neural activity tracked the number of social-call syllables before vocal onset; the corresponding relationship for echolocation-pulse count appeared after onset. This is evidence about neural activity around the animal’s own vocal production, not a decoded message or receiver response.

Location awaiting review

Observation dates not established here

Evidence, location & limits

The study used invasive, synchronized neural and ultrasonic recordings from a small captive cohort. Its acoustic classes differ in frequency as well as behavioral role, so those factors are not fully separated by the headline comparison. The reported SVM analysis uses randomized call-event-by-neuron activity instances; the reviewed methods do not establish performance on held-out bats or recording sessions. The paper does not test receiver comprehension or communicative meaning. No analysis was reproduced, and the 5.8 GiB GIN archive and analysis code were not inspected.

Reviewed the full main article through PMC, including its question, sample, acoustic and neural recording methods, category definitions, results, discussion, data/code availability and article license. Read the GIN DOI landing-page metadata only. The 5.8 GiB archive, repository file list, README, scripts, supplementary information and peer-review file were not inspected or acquired. No independent specialist reviewed this Talk2Nature assessment.

Location has not yet been checked in primary sources.

Location metadata reviewed: Not yet reviewed. Map points are approximate locators, not sample coordinates.

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2026October 2026 arXiv preprint; review status not independently checked

Machine learning · bioacoustics / Benchmark and training resource

BEANS-Next and ROOTS: a broad bioacoustics benchmark, not a translation dataset ↗

BEANS-Next evaluates 45 tasks with 37,718 examples across acoustic perception, biological-category recognition, scene understanding and in-context learning. ROOTS is a separate training resource reported to contain 43,922,135 audio-language conversations. The official project overview describes approximately 44 million pairs from 6.5 million distinct audio clips; the current Hugging Face card says its audio resolver index contains 4,745,972 distinct references. The count definitions have not been reconciled and neither is a count of independent natural animal interactions. The authors describe BEANS-Next evaluation examples as real recordings paired with human- or dataset-provided annotations; text templates and LLMs format the supported annotations into prompts rather than creating new benchmark labels. They state the benchmark audio and derivatives are disjoint from ROOTS and use held-out species where applicable. ROOTS combines curated recordings and structured/free-text metadata with audio-derived information and synthetic audio scenes. The paper reports capability gains after tier-specific fine-tuning, while its overview still identifies weaknesses in open-ended description, counting and transfer to real-world recordings.

No single mapped site

Observation dates not established here

Evidence, location & limits

This is a new arXiv preprint; review and reproduction status were not independently established. It benchmarks predictions against annotations, not whether an animal understood a signal or what it meant. ROOTS includes metadata-grounded LLM-generated text and synthetic scenes; those are useful supervision but not additional observed animal conversations. Its public overview's 6.5 million distinct-clip figure and the Hugging Face card's 4,745,972-reference index appear to describe different layers; their relationship was not established in this metadata review. The authors flag taxonomic/geographic bias and possible hallucinated species, behavior or counts. ROOTS' Hugging Face card reports license `other`, and its source audio is not redistributed; each input's rights and provenance need separate review. No resource is admitted to Talk2Nature training or commercial use.

Targeted review of the October 2026 primary preprint: abstract, task taxonomy, data construction, benchmark split description, reported results/limitations, risks and reproducibility statement; also the official project page and Hugging Face cards. Full appendices, individual data rows, source licenses, code and model artifacts were not audited or acquired. No benchmark was run.

This note covers a benchmark and training resource rather than a verified observation site.

Location metadata reviewed: 2026-10-23. Map points are approximate locators, not sample coordinates.

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2026Documentation checked

Machine learning / Dataset documentation

A benchmark is only as useful as its labels ↗

Public benchmarks cover different tasks: the BEANS dog labels identify individuals, while BEANS-Next includes acoustic-description tasks.

No single mapped site

Observation dates not established here

Evidence, location & limits

These labels do not automatically provide observed behavior or intended meaning. BEANS-Next lists noncommercial terms and source-specific licenses. Training overlap is unresolved.

BEANS task configuration and BEANS-Next card, displayed metadata and license description reviewed. No full data audit or experiment.

This note covers a tool, benchmark, method or claim review rather than one verified observation site.

Location metadata reviewed: 2026-10-03. Map points are approximate locators, not sample coordinates.

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2026Darwin Core Data Package guide issued 2026-05-26; current Audubon Core term list reviewed

Cross-taxon biodiversity data / Data standards and interoperability

Biodiversity standards can carry media and provenance, not animal meaning ↗

Darwin Core provides a biodiversity-data vocabulary and conceptual model; its 2026 Data Package guide specifies a descriptor, tabular resources and schemas whose field descriptors must come from the official per-table schemas. Audubon Core describes biodiversity-related multimedia resources and offers terms for aspects such as rights and access. Issue #759 is closed, and its visible discussion says guide section 3.4 already states the field-descriptor rule; the official review index was stale. The separate deployment gap remains: the versioned profile/schema URLs returned 404 here, while the repository's visible snapshot is unversioned and marked pre-review. These standards may support future metadata exchange, but the present Station session is not yet a protocol-defined sampling event.

No single mapped site

Observation dates not established here

Evidence, location & limits

A format conversion cannot establish that an animal was present, that a sound belongs to a species or behavior, or that a signal has meaning. Darwin Core distinguishes an organism, an occurrence and a media entity; a generic sound candidate or observer note should not be promoted to a confirmed occurrence. No mapping of current Station or Listen fields has been validated. Current device time is declared unverified, exact location is not collected, and rights for the standard-document text were not established. The observed versioned-URL failures are environment-specific; the pre-review GitHub snapshot is not evidence of final published schemas.

Reviewed the Darwin Core Data Package guide's normative requirements and versioned profile, including the requirement to use official per-table field descriptors; the conceptual model sections on Occurrence, Identification and Media; selected current Audubon Core terms covering media rights, access points, hashes and reviewer metadata; GitHub state/comments for issue #759; and the official rs.tdwg.org repository tree. The versioned profile and table-schema URLs linked by the guide returned HTTP 404 in this environment. The repository's profile and event-schema paths trace to commit e0cc2509953eeed24ad312b6e91e7bfcadd37a3b, titled 'Initial pre-review DwC-DP schemas'; the repository source is unversioned and is not used as the released 1.0 profile. No package or field mapping was generated or validated.

This is a review of data standards, not a study-site or observation record.

Location metadata reviewed: 2026-10-13. Map points are approximate locators, not sample coordinates.

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2026Documentation checked

Machine learning / Software documentation

BirdNET: identification before interpretation ↗

BirdNET supplies bird-sound identification tools and a human review workflow for detections.

No single mapped site

Observation dates not established here

Evidence, location & limits

Identification does not establish meaning. Main package code is MIT and listed models are CC BY-NC-SA 4.0; separate STM32 artifacts use Apache-2.0. Check the exact release.

Official repository and license/review documentation reviewed for the audit.

This note covers a tool, benchmark, method or claim review rather than one verified observation site.

Location metadata reviewed: 2026-10-03. Map points are approximate locators, not sample coordinates.

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2026Pinned app release

Birds / Mobile app, privacy and model-rights review

BirdNET Live keeps bird identification on the phone ↗

BirdNET Live is an open-source Flutter app for Android and iOS, with Windows described as experimental. The v1.2.1 release identifies bird species from sound using on-device BirdNET+ inference; the README describes 9,789 species, Live/Point Count/Survey/File Analysis/ARU modes, reviewable sessions and local exports. The official privacy policy says the app has no backend and does not send audio to external servers. The Getting Started guide says microphone permission is required across modes and completed sessions are saved automatically for review. Three optional network features—map tiles, reverse geocoding and weather—are separately gated and off by default; the policy says requests can reveal tile coordinates or session location/time and IP address to the respective provider. Source code is MIT and the exact bundled trained weights are declared Apache-2.0 in separate files at v1.2.1. This is a strong local-first field-recording and species-identification precedent, not evidence that an animal call has been translated or that the app understands a bird’s intent.

No single mapped site

Observation dates not established here

Evidence, location & limits

This is a documentation and release review, not an install, device test, packet capture, code audit or independent evaluation. The app classifies bird species; no evidence was reviewed here for identifying individual household parrots, call function or meaning. The model license does not clear training data, bundled images/taxonomy, dependencies or the BirdNET trademark. No training corpus was examined, and no Talk2Nature use or model transfer was tested. The official onboarding requires a microphone grant before the app is ready; this may be a poor fit for a first visit by a curious beginner. Local session auto-save and optional third-party location/weather requests need distinct user disclosure. This note does not independently verify the developer’s privacy statements or model-accuracy claims.

Reviewed official Getting Started and Privacy pages (policy updated 2026-08-06) plus the GitHub v1.2.1 release at commit 4aee47cd824873ffc9b3ca3462bf2415151a56d1, including README, LICENSE and MODEL_LICENSE. Documentation describes app flows and developer data-handling claims; these were not independently runtime-tested. No app, source archive, model, audio or training data was acquired or run.

This is a software and privacy review, not a study of one verified field site.

Location metadata reviewed: 2026-10-25. Map points are approximate locators, not sample coordinates.

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2026Publication year

Budgerigars / Edge-based acoustic monitoring study

A year of budgerigar vocal activity is not a conversation ↗

The publisher abstract describes ten budgerigars monitored under stable housing from June 2025 through May 2026. A Raspberry Pi system processed audio locally using a pretrained YAMNet feature extractor and a task-specific binary classifier. The study defined a vocal-activity second as a one-second interval containing at least one detected vocalization and reports daily and seasonal patterns. This is prior art for long-duration, edge-based vocal-activity monitoring.

Location awaiting review

Observation dates not established here

Evidence, location & limits

Only the publisher record and abstract were reviewed. The reported activity measure is not caller identity, observed social context, receiver response, comprehension or call meaning. The accessible abstract does not establish event-level accuracy, false-positive rates, the locked evaluation design, detailed welfare/ethics procedures, data availability, code or model-weight terms. No independent review or reproduction was performed. The publisher displays an Open Access label, but the article's exact reuse license was not verified here; no license for recordings, code or model weights was established.

Reviewed the publisher early-access record and abstract only. The page identifies the article as an early-access version and says complete PDF/HTML/XML versions will follow. A direct full-page request was rate-limited. Detailed methods, model validation, ethics, data availability, code, weights and supplements were not reviewed; no files were acquired or run.

Location has not yet been checked in primary sources.

Location metadata reviewed: Not yet reviewed. Map points are approximate locators, not sample coordinates.

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2026Dataset version publication year

Birds / Passive acoustic dataset

CEB can benchmark bird sound detection, not what a call means ↗

CEB v1.1 is a large library of bird recordings with human-marked sound events: 20,826 audio files and 92,857 annotations across 256 eBird species codes and 21 vocalization types. It can help test whether software finds and sorts labeled calls in recordings. It contains no independent observations of a listener's response or ground truth about what a bird meant, so it cannot test translation. The field soundscape recordings are more relevant to passive-monitoring workflows than the separate focal Xeno-Canto recordings.

Germany (country-scale reference) · Greece (country-scale reference)

Observation dates not established here

Evidence, location & limits

The full collection is 49.7 GB. train_soundscape uses weak point-centered labels with a fixed +/-2.5 second window; many rows have no vocalization type. Its annotation CSV is 8,559,233 bytes, above the collection tool's current 5 MiB per-file cap, so its anonymized project-group semantics and row distribution remain unknown. The smaller 2,401,049-byte test_soundscape.csv contains public test labels and was not inspected; it is not a concealed locked test, and a separate untouched evaluation would be needed. The data are mostly from Germany with a smaller Greek subset and are not a parrot-specific context corpus. The record gives CC BY 4.0 for soundscape audio and all dataset metadata/annotations, while train_xenocanto retains per-record Xeno-Canto licenses; the schema says some of those license fields are empty. The schema also identifies 40 zero-area boxes that must be excluded from localization tasks and one truncated Xeno-Canto audio file whose annotations extend beyond its duration. No project/site leakage, source overlap, annotation reliability or source-media privacy audit has been performed.

Reviewed the official Zenodo v1.1 landing page, published file inventory, component-specific rights statement and schema.json browser preview. The v1.1 notes identify annotation and taxonomy corrections; the prior v1 record 20762099 is superseded. Re-read the official Zenodo API inventory on October 23, 2026: train_soundscape.csv is 8,559,233 bytes (provider MD5 55b5e3f99566c5bb3ba4fb5b833424da) and test_soundscape.csv is 2,401,049 bytes (provider MD5 f572155ae41714e17b218e95d97b1ce9). These checksums are upstream metadata, not locally verified. No CSV rows, audio, code or weights were downloaded, played, parsed or executed; no detector was evaluated.

The Zenodo v1.1 record says the geolocated soundscape coverage is predominantly Germany with a smaller subset from Greece; those metadata coordinates are rounded to whole degrees. The focal Xeno-Canto subset has no coordinates. These country references do not show individual sites, species-specific presences or recorder/animal locations.

  • Germany (country-scale reference) · Study region

    The dataset record names Germany as the predominant recording country. The rounded GeoNames reference represents only the stated soundscape coverage, not an exact site or individual recording.

    Location source ↗ · Geographic Coverage, Zenodo v1.1; GeoNames feature 2921044 for the country-scale reference
  • Greece (country-scale reference) · Study region

    The dataset record identifies a smaller Greek recording subset. The rounded GeoNames reference represents only the stated soundscape coverage, not an exact site or individual recording.

    Location source ↗ · Geographic Coverage, Zenodo v1.1; GeoNames feature 390903 for the country-scale reference

Location metadata reviewed: 2026-10-25. Map points are approximate locators, not sample coordinates.

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2026arXiv v2 posted May 21, 2026; paper described as under review in the pinned code-repository README checked October 13, 2026

Ecoacoustics · machine learning / Soundscape classification and model-evaluation evidence

CoarseSoundNet: soundscape context, not animal language ↗

CoarseSoundNet targets broad, potentially co-occurring soundscape layers: anthropophony (human-made sounds), biophony (animal sounds), geophony (natural abiotic sounds) and an explicitly handled silence condition. The manuscript reports strong EDANSA-2019 test performance but a marked drop on the different BEsound recording domain, especially for human and geophysical sounds. Its best BEsound macro-F1 of 0.799 applies class-specific thresholds and window-count settings selected on BEsound itself; it should be treated as a tuned evaluation result rather than an untouched holdout estimate. The ecological case study uses model predictions to pre-filter audio before calculating conventional acoustic indices. This can help characterize recording context, but it does not say which species vocalized or what a call means. The paper uses public EDANSA together with BEsound and other collections that are not all directly accessible. Code, weights, article and data have distinct licenses: the pinned training repository declares MIT, the Hub model declares CC BY-NC-SA 4.0, the arXiv paper declares CC BY-NC-SA 4.0, and Zenodo's API declares CC BY 4.0 for the EDANSA record while the rendered license field is blank.

No single mapped site

Observation dates not established here

Evidence, location & limits

The paper remains a preprint in the records reviewed: the author-group GitHub README says it is under review, and an author's publication list places it under preprints; a peer-reviewed version was not verified. The BEsound evaluation data also informed selection of post-processing thresholds/counts, which limits the interpretation of the best reported score as independent generalization. Domain, recorder and annotation differences matter; reported errors include insect sounds confused with biophony, wind/background noise around silence/geophony, and masking when classes co-occur. Some training collections are private or available only on request. The code repository is marked under construction. Model weight reuse is non-commercial under its declared CC BY-NC-SA terms; underlying encoder, dependencies and all dataset components need separate review. No Talk2Nature result is reproduced.

Reviewed the full arXiv v2 HTML: data composition, model comparison, transfer to BEsound, silence handling, threshold/duration post-processing, error analysis and ecological-index case study. Reviewed the pinned GitHub repository/README/license, Hugging Face revision and file/rights metadata, and the Zenodo record plus read-only API metadata. No code, model weights, dataset archive, audio, figures or supplementary files were downloaded or run.

This note reviews a model, datasets and evaluation method rather than a verified observation or study-site record.

Location metadata reviewed: 2026-10-13. Map points are approximate locators, not sample coordinates.

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2026Published 8 June 2026; version of record 20 August 2026

Plants & fungi / Controlled plant–fungus isotope experiment

Common endophyte networks: resource transfer is not communication ↗

In split-plate laboratory experiments with Arabidopsis thaliana, Waschk et al. report that three tested endophytes formed common endophyte networks and moved some isotope-labelled resources from donor-side soil into receiver plant tissues. The paper reports 55% higher amino-acid-derived 15N in receiver plants for Fusarium temperatum and a smaller 13C enrichment for Trichoderma viride (+2.83% over control); it also reports 38% greater plant biomass for F. temperatum in this setup. These are bounded physiological and isotope outcomes, not a decoded signal or a demonstrated plant conversation.

No single mapped site

Observation dates not established here

Evidence, location & limits

The experiment used a controlled split-Petri-dish system, Arabidopsis hosts and three fungal endophytes, not a forest or natural field network. The design lacked a no-donor treatment and some no-receiver isotope controls; the authors could not distinguish direct hyphal 15N delivery from fungal exudate or necromass entering receiver soil. Deuterium appeared in plant isotope controls, indicating an abiotic transfer pathway. The paper says natural-system CEN involvement remains untested. It measured resource enrichment and plant physiology, not a sender-derived information signal, receiver behavior or interpretation; resource transfer cannot establish plant-to-plant communication. CENs are distinct from common mycorrhizal networks, so this study does not settle the forest mother-tree debate. The linked data and code have no declared license in the reviewed repository metadata and were not inspected.

Full publisher article reviewed: abstract, introduction, results, materials and methods, statistical analysis, data-availability statement, limitations and rights. Official GitLab project metadata, current main commit, root tree and per-file metadata were checked; no dataset bytes, R code, README or supplementary file was retrieved or run.

Controlled laboratory study with no field collection or reported study-site location; author affiliations are not used as collection sites.

Location metadata reviewed: 2026-10-13. Map points are approximate locators, not sample coordinates.

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2026Pinned repository snapshot c412c5c, commit dated 2026-05-26

Cross-taxon bioacoustics / Dataset discovery catalog

A community directory for discovering bioacoustic datasets ↗

The directory is a useful cross-taxon discovery lead and a reminder to record dataset version, license, annotation level, recording type, geography and other metadata. The project says missing information is being added over time, so its entries should be treated as leads that require verification at the dataset's original source.

No single mapped site

Observation dates not established here

Evidence, location & limits

This is a community-maintained catalog, not a dataset, a license-clearing service or a scientific validation. Its entries may be incomplete or change. At the reviewed commit, the repository has no recognized software license declaration; public visibility does not establish permission to copy or run its code. No individual dataset's terms, consent, privacy, sensitive locations, label quality, provenance or split integrity were reviewed. No files were acquired.

Reviewed the README and root inventory at commit c412c5c3fd65552d17e492dac35f2be2c30349a4. The README describes a community list of online bioacoustic datasets intended for machine-learning use, its metadata fields, and how people can submit corrections or additions. GitHub API metadata reports no recognized software license, and the pinned root inventory has no LICENSE file. No dataset entry, linked file or model was acquired, installed, copied or run.

This note reviews a dataset discovery catalog, not a location-specific animal study.

Location metadata reviewed: 2026-10-13. Map points are approximate locators, not sample coordinates.

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2026Talk2Nature literature and metadata synthesis

Companion animals / Comparative evidence review

Which companion animal is ready for a first study? ↗

Among the reviewed resources, no public companion-animal corpus combines naturally occurring household audio, independently time-aligned behavior or receiver-response labels, verified household/individual/session grouping, and fully reviewed reuse terms. CatMeows describes 440 sounds from 21 cats with context and owner/session identifiers, but its CC BY-NC 4.0 terms do not provide general commercial permission and the contexts are elicited. A dog-bark paper analyzes 16,789 segments from 74 dogs during designed home exposures; its reported classifier predicts associations with those situations, not what dogs mean. Parrot surveys describe caregiver reports about human-language mimicry and names, not an acoustic training corpus. A budgerigar research deposit contains derived call features and separate social observations without source call WAVs or event-aligned behavior; a separate 2026 abstract reports local vocal-activity monitoring, not a conversation model.

Location awaiting review

Observation dates not established here

Evidence, location & limits

The comparison covers only the named, already reviewed records and cannot establish that no other suitable dataset exists. Reported record counts are not independent sample counts. Dog contexts were linked to designed stimuli, CatMeows files were not acquired, parrot questionnaire workbooks were not inspected, and the budgerigar edge paper's full methods were unavailable in this review. No files, recordings, labels, code or weights were acquired or run; no animal-carer interviews or independent scientific review occurred.

Compared the existing full-paper review of dog-bark classification; CatMeows record and rights metadata; full-text review and deposit metadata for two companion-parrot surveys; primary methods and official deposit inventory for budgerigar vocal/sociality data; and the publisher abstract for a 2026 edge-monitoring study. Supporting survey workbooks and third-party media were not acquired or inspected. This is a library-bounded comparison, not a systematic review or independent specialist assessment.

Location has not yet been checked in primary sources.

Location metadata reviewed: Not yet reviewed. Map points are approximate locators, not sample coordinates.

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2026Study year

Parrots / Multi-species owner survey

When companion parrots use names: what a survey can and cannot show ↗

The survey reports on 1,202 companion parrots across 89 species; phrase-use information was supplied for 884 birds, with names reported for 413. Multiple observers identified 131 reported examples of appropriate name use across 88 birds and 30 species, and 69 individualized examples across 42 birds and 19 species.

Location awaiting review

Observation dates not established here

Evidence, location & limits

These are human survey reports about learned human-language phrases, not recordings or experiments on natural parrot calls. Context was supplied or interpreted by respondents, reports were not representative, and the authors state that scoring remained subjective and does not unequivocally demonstrate understanding. The abstract says 802 example phrases contained names while the Results section says 801; the May 2026 correction adds ORCID identifiers only. Methods say the survey requested both respondent and parrot names. The article describes its S2 phrase dataset as de-identified, but we have not inspected that workbook or independently assessed disclosure risk; review the exact files and terms before reuse.

PLOS ONE article HTML reviewed for abstract, methods, results and discussion; the official correction notice was checked. Supporting information, raw survey data and figures were not downloaded or inspected, and no independent reanalysis was performed.

Location has not yet been checked in primary sources.

Location metadata reviewed: Not yet reviewed. Map points are approximate locators, not sample coordinates.

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2026Publication year

Parrots / Supplementary survey dataset

Companion-parrot S2: reported context is not a call label ↗

PLOS describes S2 as a spreadsheet of parrot name phrases with coding and de-identified supporting information. The paper says phrase-use context was based on survey-takers' accounts of when or how a phrase was used, not inferred from the phrase's words; observers coded and reconciled the reports. The NLM catalog reports CC BY 4.0 for the linked Figshare item.

Location awaiting review

Observation dates not established here

Evidence, location & limits

This is survey-derived phrase and context metadata, not natural-call audio or a receiver-response experiment. Respondents were asked for respondent and parrot names. Although the article describes S2 as de-identified, the workbook and its fields were not inspected, so re-identification risk and consent scope are unknown. The cataloged license does not resolve those issues. Do not treat caregiver context or researcher coding as independent ground truth or as model training labels.

Reviewed the primary PLOS paper's methods and S2 description and the NLM Dataset Catalog record, which links the S2 DOI to PLOS Figshare item 32046233 and reports CC BY 4.0. The direct Figshare page/API were not retrievable in this review. No workbook bytes, schema or cell contents were inspected; no reanalysis was performed.

Location has not yet been checked in primary sources.

Location metadata reviewed: Not yet reviewed. Map points are approximate locators, not sample coordinates.

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2026GitHub repository snapshot at pinned commit; Zenodo dataset v1.0.0

Cattle / Mobile app, dataset and machine-learning methods review

CowRumIA: a cattle-audio app and a test-set caution ↗

CowRumIA is a 2026 undergraduate project pairing a Flutter Android app with YAMNet embeddings and a dense classifier for four cattle vocalization categories. The Zenodo v1 record describes 624 labeled WAVs; the README reports 194 stressed beef, 39 calm beef, 350 stressed dairy and 41 calm dairy samples and advertises 85.6% accuracy on 125 test audios. In the pinned training script, a random stratified 80/20 utterance split creates X_test, then the same X_test and y_test are supplied to training as validation data while callbacks select epochs and adjust the learning rate; predictions are later evaluated on that same set. The reported score therefore is not an untouched final-test estimate. The size or direction of any resulting optimism cannot be determined from this review. This is a useful mobile-implementation comparator and a strong evaluation-design lesson, not evidence of animal communication or a validated welfare diagnostic.

Location awaiting review

Observation dates not established here

Evidence, location & limits

The four output classes jointly encode cattle production type and stress/calm category, and the README class counts are imbalanced (350 in the largest class versus 39 in the smallest). The split code is utterance-level and does not group by animal, session or site; the reviewed materials do not establish whether repeated animals or recording conditions cross splits. Zenodo states expert validation but the reviewed record does not provide the annotation protocol, inter-rater agreement or independent operational welfare ground truth. The dataset contains mixed-source material, but upstream, file-level reuse terms are unresolved; Zenodo shows only a copyright notice. GitHub declares MIT for repository software, while the packaged TFLite assets and underlying YAMNet/model-weight provenance and terms were not separately verified. The README describes an intensity-weighted stress-level output and a confidence threshold, but the relationship to independently measured welfare severity is not established here. Its documented training command points to a file that returned 404 at the pinned commit. No effect size, clinical/welfare utility, device reliability, field performance or cross-site generalization was reproduced. These are limits of the reviewed evidence, not proof that the project authors have no additional records.

Read the pinned CowRumIA README and LICENSE, statically inspected model/train_model.py around its split, callbacks and final evaluation, and reviewed the Zenodo v1 record description, file listing, dates and Rights section. The repository snapshot was pinned to commit 44fdc5e5cf3f080457211bb87eb8cb2fc6b099ac. No dataset or model files were downloaded, no code was run, no app was built, no thesis full text was reviewed, and no independent specialist review or reproduction occurred.

Zenodo names two Aguascalientes collection sites, but the reviewed metadata does not map individual recordings to sites or establish locations for mixed-source additions. No atlas marker is added.

Location metadata reviewed: 2026-10-14. Map points are approximate locators, not sample coordinates.

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2021–2026Source year

Cross-species / Annotation software

DAS: build on an existing annotation workflow ↗

Deep Audio Segmenter connects manual labels, model training, automatic proposals and human correction in an existing audio workflow.

No single mapped site

Observation dates not established here

Evidence, location & limits

Suggested labels still need checking. A quick-start training split is not a validation protocol for our intended species or new recording conditions.

Official introduction, repository README and bird tutorial through proofreading. The primary performance paper and current package were not evaluated.

This note covers a tool, benchmark, method or claim review rather than one verified observation site.

Location metadata reviewed: 2026-10-03. Map points are approximate locators, not sample coordinates.

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2026Scientific Data 13, Article 770 (2026); version of record 22 May 2026

Birds · Dupont's lark (Chersophilus duponti) / Dataset descriptor

Dupont's lark songs and calls: an annotated vocal-type dataset ↗

The article describes standardized recordings from 20 Spanish populations and reports 4,297 annotated songs from 191 singing males, representing 401 song types, plus 795 calls from 97 calling males, representing 80 call types. Experts categorized acoustic song and call types and provide event timing, frequency bounds and individual IDs. One ornithologist performed the primary annotation; uncertain classifications and types reported as shared among populations received additional expert review. Zenodo resolves the article's cited concept DOI 10.5281/zenodo.17264916 to current version record 10.5281/zenodo.17264917; the API identifies 17264916 as conceptdoi, 17264917 as the version doi, and declares record-level CC BY 4.0. Its eight-file metadata inventory includes provider-supplied sizes and MD5 values. The separate Digital.CSIC distribution also identifies version DOI 17264917 and declares CC BY 4.0. The CSIC archive bytes and their relationship to the Zenodo file set remain unverified.

Spain · 20 study populations (country-level locator)

March–June 2024 and 2025

Evidence, location & limits

The study covers 20 populations in Spain. Birds were not individually marked; measures reduced but do not prove that repeat recordings are independent, and song/call individuals could not be matched. One expert made the primary annotations; uncertain or population-shared types received additional review. Labels are acoustic type categories, not independently observed behaviors or word meanings. No published grouped ML split was reviewed. The authors advise against broad frequency-feature comparisons because of spatial dialect variation. Zenodo's per-file MD5 values are provider metadata and were not locally verified. The 3,711,242,291-byte CSIC archive was not fetched or checked against the eight-file Zenodo inventory; a size comparison alone would not establish matching contents.

Reviewed the full publisher version of record, including Abstract; Methods (study area, recording, annotation and vocalization identification); Data Record; Technical Validation; Usage Notes; Data Availability; Code Availability; and Rights and Permissions. Also reviewed the official Zenodo API response: concept DOI 17264916 returned HTTP 302 to version record 17264917, whose JSON declares conceptdoi 17264916, version DOI 17264917, record-level CC BY 4.0 and eight files with provider-supplied sizes and MD5 values. Reviewed Datos.gob.es metadata, the full 4,169-byte Digital.CSIC README and archive HEAD metadata. No dataset archive, code, article file or figure was saved or executed; provider checksums were not locally verified.

One country-level Spain locator represents 20 study populations; no individual site pins are shown. The paper says the threatened species' metadata coordinates are rounded to about 1 km.

  • Spain · 20 study populations (country-level locator) · Study region

    The article describes 20 study populations in Spain and says the recorded-male coordinates in its metadata are rounded to about 1 km because the species is threatened. The atlas shows only a country-level locator, not individual study points.

    Location source ↗ · Methods: Study area; Data Record (Metadata.csv description)

Observation-date source ↗ · Methods: Study area

Location metadata reviewed: 2026-10-12. Map points are approximate locators, not sample coordinates.

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2024–2026Source year

Elephants / Study and reassessment

Elephant calls: pair promising findings with their critiques ↗

The 2024 study combines acoustic classification with receiver-response experiments to investigate individually directed calls.

Kenya (country-scale reference)

Observation dates not established here

Evidence, location & limits

Name-like is a bounded interpretation, not a recovered vocabulary. A 2026 reanalysis questions statistical evidence from the classification analyses; that dispute is not resolved by this note.

Selected discussion and playback methods in the author-organization PDF; Dryad landing page. The 2026 reanalysis was located through a publisher excerpt and related-work listing, not read in full.

The primary paper identifies fieldwork in Samburu and Amboseli. One country-level locator represents multiple regions; it is not a field site.

  • Kenya (country-scale reference) · Study region

    The primary paper identifies fieldwork in Samburu and Amboseli. One country-level locator represents multiple regions; it is not a field site.

    Location source ↗ · Acknowledgements and author contributions

Location metadata reviewed: 2026-10-03. Map points are approximate locators, not sample coordinates.

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2026Source year

Plants & fungi / Measurement study

Fungal electrical signals travel; communication remains untested ↗

Adamatzky (2026) reports slow, ordered electrical events across eight differential channels placed along one Pleurotus ostreatus-colonised substrate, recorded for about fifteen days. The paper interprets lead–lag structure as propagation consistent with ionic waves.

Location awaiting review

Observation dates not established here

Evidence, location & limits

Eight channels on one specimen are spatial measurements, not eight biological replicates. The Methods report no external stimulation and do not describe a sham-electrode or uncolonised-substrate comparison; the shuffled-spike control tests temporal ordering within this recording, not a receiver response or communicative meaning. The paper cites a 2021 Zenodo record with one large archive, but the exact analyzed file is not identified and no dataset license is displayed.

Full text of the 2026 primary paper and its Methods, limitations and data statement reviewed, alongside the official cited Zenodo record. The paper's eight channels come from one specimen; the 2021 record lists one 413.2 MB archive but does not map the analyzed 15-day series to a file. The dataset license is not displayed. No files were downloaded, no analysis was reproduced and no independent specialist review was received.

Location has not yet been checked in primary sources.

Location metadata reviewed: Not yet reviewed. Map points are approximate locators, not sample coordinates.

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2026Peer-reviewed Science article, published July 23,

Passerine birds (songbirds) / Global community-science acoustic analysis

A global bird-song study finds shared acoustic patterns—not a universal bird language ↗

This is a strong example of large citizen-science sound collections enabling a useful cross-species analysis. The paper reports eight recurring acoustic motifs in songs from thousands of passerine species and associations between motif distributions, species traits and sound-propagation conditions. The author repository describes a 37-dimensional acoustic feature space built from modulation power spectra and unsupervised clustering. This is evidence about acoustic form and broad ecological patterns; the reviewed sources do not establish call meanings, receiver comprehension or a general bird-to-human language model.

Location awaiting review

Observation dates not established here

Evidence, location & limits

The study's apparent scale does not make its recordings a single rights-cleared training set. The pinned Git tree contains 1,280 audio-segment files (185,758,300 provider-reported bytes), not 1,280 verified independent recordings; the metadata payload and filename-to-source joins were not inspected. The GBIF record is a metadata distribution subset with its own CC BY-NC 4.0 declaration; it is not a blanket license for underlying audio. Xeno-canto recording pages display item-level terms, illustrated by CC BY-NC-ND 2.5 and CC BY-SA 4.0 examples, and the exact study-recording membership and each source license were not checked. The author repository README states CC BY 4.0, but the tree has no standalone LICENSE file and the README does not resolve the license scope for code, derived tables, maps and third-party audio. Its Zenodo DOI remains a placeholder. The paper methods, exact source IDs, quality-control rules, annotations, class balance, split design, geographic/recordist sampling and full license chain remain unaudited. This analysis is prior art for global passerine acoustic motifs, not a new Talk2Nature result or a semantic-communication benchmark. The filename screen yields 495 numeric-ID candidates, but only three were spot-checked and only one filename/taxon pair matched; it does not prove all filenames map to source recordings or that any sample belongs to the paper's analyzed set. Sample license terms vary (two CC BY-NC-ND 4.0, one CC BY-NC-SA 4.0). Official API access requires a key and is rate-limited; no full metadata/license census is available.

The PubMed abstract reports an analysis of acoustic song architecture across more than 3,000 passerine species and eight recurring motifs associated with species traits and sound-propagation conditions. The authors’ repository README identifies 116,792 song bouts from 3,160 species, describes modulation-power-spectrum features and lists analysis scripts and derived-data files. A recursive GitHub API tree check at pinned commit 1629e665f902cad405ff2f7bf2a0e9dc8c09b47e was not truncated and listed 1,456 tracked files, including 1,280 audio segments totaling 185,758,300 bytes under `data/selected_data_8x8x20_top/`; the tree also lists a 12,796,164-byte `metadata_xeno_passerines.csv` and a 79,543,989-byte `traits_data.csv`. These are provider-reported file sizes and paths only: no file contents or audio were fetched. The tree has no standalone LICENSE file, and the README’s Zenodo data link is the literal placeholder 10.5281/zenodo.XXXXXXX. The GBIF Xeno-canto dataset record describes a metadata subset and a dataset-level CC BY-NC 4.0 declaration. Two unrelated Xeno-canto record pages display different recording licenses; their membership in this study was not checked. No full methods/supplement review, recording-level source/license mapping, data acquisition, code execution, model run or independent reproduction was performed. A follow-up filename screen matched all 1,280 audio paths to a numeric-ID-like segment pattern, yielding 495 distinct candidates. Three path-selected record pages confirmed candidate numbers resolve to Xeno-canto pages; one filename taxon matched the page taxon. Their sample terms were CC BY-NC-ND 4.0 (two) and CC BY-NC-SA 4.0 (one). This validates only the pattern and three examples, not all candidate joins or study membership. The official Xeno-canto API documentation requires a key and warns of strict rate limits; no key or bulk API request was used. No coordinates were extracted or retained.

Location has not yet been checked in primary sources.

Location metadata reviewed: Not yet reviewed. Map points are approximate locators, not sample coordinates.

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2026Publication year

Great green macaw (Ara ambiguus) / Vocal development and population variation

Macaw calls change with age and population. That is not a translation. ↗

Allison, Jimenez and Wright compare great green macaw adult contact calls across wild, captive and reintroduced Costa Rican populations and describe nestling call development. The publisher abstract reports minimal adult call variation among the three population categories and variation in nestling calls during mid-development. The authors discuss morphology, call overproduction, selective attrition and vocal convergence as possible explanations. This concerns acoustic development and variation, not a demonstrated call meaning.

Costa Rica

Recordings: 2023–2024

Evidence, location & limits

The study uses one region in Costa Rica and three population categories. Adult birds were not individually marked, and population individual counts are minimum estimates. The Dryad record lists selection tables, acoustic measurements, derived analysis files and R scripts. Its README says extended-selection RDS objects combine selected calls with corresponding sound files through warbleR and use sound-file names; the files were not inspected, so it remains unknown whether audio samples are embedded or referenced. The paper says sound files can be obtained from the corresponding author on request. No public behavioral event labels, receiver-comprehension test, independent Talk2Nature review or reproduction was established. This cannot support a context-prediction or translation benchmark.

Reviewed the publisher abstract and exposed section snippets; the publisher page itself was not fully accessible. Also reviewed the Dryad record, abstract, README descriptions and file inventory, and Dryad’s current data-reuse policy. The README says the RDS selection objects combine call selections with corresponding sound files through warbleR, but their contents were not inspected. No article, data, audio, code or supplementary bytes were acquired or executed.

The study record names Costa Rica and compares wild, captive and reintroduced populations. The public atlas uses the existing country-level locator and omits sites, nests and coordinates.

  • Costa Rica · Study region

    The public map shows only Costa Rica at country precision. No exact population, nest or recording coordinates are added.

    Location source ↗ · Publisher abstract: three populations in Costa Rica; Dryad README describes recordings made in Costa Rica.

Observation-date source ↗ · Dryad README description: adult and nestling recordings in 2023 and 2024.

Location metadata reviewed: 2026-10-12. Map points are approximate locators, not sample coordinates.

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2026bioRxiv v2 preprint; Edmond dataset v1

Spotted hyenas / Preprint and sound-event dataset

HyenaSET: a strong sound-event benchmark, not a communication test ↗

HyenaSET is a useful recent sound-event methods comparator: its Edmond v1 metadata describes 590,480 ten-second clips, about 1,640 hours from 19 spotted hyenas, with 87,394 expert-reviewed clips (about 243 hours). The annotations mark vocalization intervals and types, and distinguish calls from the collar-wearing focal hyena from nearby non-focal callers. The preprint provides sample-stratified splits and a four-individual held-out split, then reports an animal2vec baseline. These targets concern event detection, call categories and source attribution; they are not independently observed receiver responses or evidence of message meaning.

Kenya (country-scale reference)

Observation dates not established here

Evidence, location & limits

The bioRxiv manuscript is not peer-reviewed. Edmond's v1 archive is one 107,184,528,475-byte TAR.GZ with a provider MD5 that Talk2Nature has not locally verified. Its dataset-specific terms are CC BY-NC-SA 4.0, so the data are not admitted to the shared commercial-capable training path; the preprint, animal2vec code and model weights have separate rights. The record describes a CSV that maps randomized names to individual identities and recording times for four test individuals; no rows or precise locations were reviewed, so wildlife-location sensitivity remains unresolved. The preprint says animal2vec is pretrained on all available audio before fine-tuning on the first cross-validation split. That wording implies exposure to audio from held-out partitions; our methods interpretation is that the reported result is transductive unless the authors clarify otherwise, not a strict unseen-audio baseline. No independently observed behavior or receiver-response labels were established by this review.

Read the bioRxiv v2 abstract and targeted data-collection, annotation, split, benchmark and data/code-availability sections. Examined official Edmond and DataCite metadata for version 1, release date, archive size, provider checksum and dataset terms. The paper is a preprint, not peer-reviewed. No audio, labels, code or model weights were downloaded, installed or run; no data rows, coordinates or individual timestamps were inspected. No independent review or reproduction occurred.

The bioRxiv v2 abstract identifies collar recordings from spotted hyenas in the Maasai Mara National Reserve, Kenya. The atlas reuses only a country-scale Kenya reference; it does not plot the reserve, individuals, timestamps or recording coordinates. The dataset metadata links some randomized clips to four individuals and times; no rows or precise locations were inspected.

  • Kenya (country-scale reference) · Study region

    The bioRxiv v2 abstract names the Maasai Mara National Reserve, Kenya. The map uses the country-level GeoNames reference, not the reserve or a recorder position.

    Location source ↗ · Abstract

Location metadata reviewed: 2026-10-25. Map points are approximate locators, not sample coordinates.

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2026Peer-reviewed Scientific Data article

Insects (Orthoptera and Cicadidae) / Multi-species acoustic identification dataset

A large insect sound dataset can test species recognition—not what a call means ↗

InsectSet459 is a substantial, multi-species resource for insect sound identification and testing audio pipelines across varied sample rates. Its species labels do not identify behavior, caller intent, receiver response or message meaning. The recording diversity also means a phone microphone should not be assumed to capture every insect signal; high-frequency work may need a separately validated sensor.

Location awaiting review

Observation dates not established here

Evidence, location & limits

The dataset is weakly labeled at the recording level, and its published split is described as balanced by files and audio duration rather than as a held-out contributor, locality or device evaluation. The full test archive and annotations are published, so this is not a secret locked test set. The 6.9 MB annotation CSV exceeds Talk2Nature’s 5 MiB per-file collection limit; the 83.7 GB archive is not a bounded acquisition. The dataset description lists CC 4.0 or CC0 source recordings, but exact source-level terms and attributions still need to be retained for any selected file. The linked curation-code repository has no located standalone license. No Talk2Nature reproduction, hardware test or insect-behavior review has been performed.

The paper abstract reports 26,298 recordings (226.6 hours) from 459 Orthoptera and Cicadidae species. The Zenodo v1.1 record lists an 83.7 GB package, including a 6.9 MB annotation CSV, and describes source recordings as CC licenses 4.0 or CC0. The record states a 60/20/20 split balanced by file number and duration. The paper describes broad recording sample rates, from 8 to 500 kHz. Approximately one third of files use sample rates above 44 kHz according to the paper. The full paper methods, split membership, original annotation rows, source-level license manifest, location values and code were not inspected.

Location has not yet been checked in primary sources.

Location metadata reviewed: Not yet reviewed. Map points are approximate locators, not sample coordinates.

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2026Publication year

Cattle / Mobile app, curated audio library and machine-learning workflow review

MooLogue separates cattle-call learning from live analysis ↗

Kate and Neethirajan describe MooLogue as a Flutter mobile application for learning about cattle vocalizations. The paper reports a curated library built from 569 clips, 48 fine-grained behavioral classes and three commercial dairy farms, grouped into nine high-level learning categories. Its published v1.0 is described as an offline-first educational app with curated examples and metadata bundled for normal use. The pinned public repository README instead describes a cloud workflow that uploads recordings to AWS, runs PyTorch inference and returns a label, confidence and recommended action. The repository snapshot is dated 2025-11-25, and the paper does not establish that this exact snapshot is the implementation of the later v1.0. Treat the discrepancy as unresolved. This is a useful mobile-learning and version-control case study, not evidence that an app can translate cattle calls or provide validated welfare advice.

Location awaiting review

Observation dates not established here

Evidence, location & limits

The article was reviewed selectively from an author-uploaded copy, not in full through the publisher; figures and supplemental artifacts were not inspected. The paper says the audio and metadata are restricted under a data access agreement to protect farm confidentiality; the direct Zenodo API request failed, and no item manifest or file-level terms were checked. The pinned GitHub tree has no root LICENSE file although its README metadata says MIT and GitHub reports no detected license. No exact mapping from that 2025 source snapshot to the 2026 published app release was established. No app store installation, source build, upload, classifier, model weights or welfare recommendation was tested. The app paper is not an independent usability study or proof of interpretive accuracy, and the project does not validate open-ended communication or generalization to other farms/species.

Reviewed selected abstract, app design and release, curated-corpus description, dataset-access statement and article license in an author-uploaded article copy. Read the README and README_softwarex.md at pinned GitHub commit 8023dbdfed8587d07eeafa54accb68408ffaefbe. The Zenodo API metadata request returned HTTP 406, so its manifest was not independently inspected. No app was installed, no repository archive or dataset payload was acquired, and no code or model was run.

The article names three commercial dairy farms but the locations and site-to-recording mapping were not established. No public map marker is supported.

Location metadata reviewed: 2026-10-25. Map points are approximate locators, not sample coordinates.

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2026Source year

Plants & fungi / Behavioral experiment

When another organism responds ↗

In controlled choices, female Egyptian cotton leafworm moths (Spodoptera littoralis) changed where they laid egg clusters when playback of drought-stressed tomato-plant clicks was present. The preference depended on the other cues: playback attracted oviposition in a plant-free arena, while moths preferred the silent side when healthy tomato plants were offered on both sides. Deafening removed the plant-free playback preference; male-moth click controls did not produce a detected preference. A separate long-arena experiment used a lower 30-click/min rate and found egg clusters concentrated near the playback and feeder rather than the silent edge. A checksum-matched aggregate audit of the single Mendeley data.xlsx file also matched the five main Table 1 tuples for repetitions, paired arena/date observations and egg-cluster totals; this is a source-table consistency check, not a statistical-model reproduction.

Location awaiting review

Observation dates not established here

Evidence, location & limits

This is one moth species, one plant species and a controlled laboratory/greenhouse study, not evidence that plants intentionally communicate or that animals can be translated by AI. The authors characterize drought sounds as a likely cue rather than an evolved message. The main choice experiments used 60 clicks/min; the authors measured at most 20 clicks/min from 45 drying seedlings in a 50-hour recording and argue that a dense patch could raise exposure. The later 30-click/min gradient setup differs in arena and subject design, so it is not a direct replication of the main choice trials. Preferences also reverse with plant context; other visual, olfactory and tactile cues complicate interpretation. The Mendeley inventory contains tabular and XY-point files, not listed audio recordings; it cannot serve as an animal-audio benchmark. Although one workbook was checksum-verified and parsed, this does not verify the remaining files, cross-file joins, dependencies or correspondence to the publisher ZIP. Reviewer comments raised ecological realism, effect-size presentation, availability of raw data/code and species generalization; Talk2Nature has not independently reproduced or reanalysed the results. The workbook contains two additional condition labels outside the five Table 1 groups compared here. In particular, its separate Bat oviposition group is not mapped to a reported oviposition outcome in the reviewed article, which mentions bat playback only in a flight-response validation; exclude that group from interpretation pending source clarification. The lower-rate workbook condition is not a direct replacement for the main 60-click/min choices. The full eLife source-data ZIP and the rest of the Mendeley files remain unacquired.

Full version-of-record text reviewed through the NCBI PMC copy and eLife PDF, including main methods, figures, public reviewer comments and author responses. The public Mendeley Data v1 inventory lists three folders with 32 files (24 CSV, four R, two MATLAB and two XLSX); its page and file panels were reviewed separately from the article. On October 24, one 51,160-byte data.xlsx file was acquired to ignored external storage and matched the Mendeley file-panel SHA-256. A standard-library aggregate-only audit matched all five main Table 1 repetition, paired-observation and egg-cluster totals. No spreadsheet formulas or deposited code were executed, no inferential model was fit, the other files and publisher ZIP remain uninspected, and no independent specialist review was obtained.

Location has not yet been checked in primary sources.

Location metadata reviewed: Not yet reviewed. Map points are approximate locators, not sample coordinates.

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2025–2026ICLR 2025 paper; NeurIPS 2025 workshop follow-up; official demo documentation updated April 9, 2026

Machine learning · bioacoustics / Audio-language model and benchmark evidence

NatureLM-audio: a bioacoustic model, not a nature translator ↗

NatureLM-audio combines a BEATs audio encoder with Llama 3.1 8B Instruct and responds to audio-plus-text prompts with outputs such as species labels, detections, call types, life-stage classes and captions. Its ICLR 2025 paper reports strong results on several BEANS-Zero tasks and evaluates held-out taxa; the paper cautions that BirdNET and Perch comparison datasets overlap their training data, so those are topline rather than fully fair comparisons. A NeurIPS 2025 workshop follow-up preprint exposes prompt-format brittleness: asking for common and scientific names together reduced accuracy to 6–12% on two benchmark components compared with prompts asking for one name. For those combined-name prompts, the authors report that LoRA interpolation at about α=0.7 raises accuracy from 6% to 45% on Watkins and 12% to 63% on CBI. Separately, on one 425-sample, 40-class closed-set unseen-family task, reported F1 rose from 0.09 to 0.28 (the authors describe this as a >200% relative improvement). These are bounded classification and instruction-following results, not evidence that the model discovers what an animal means or can hold a conversation with it. The current training-data viewer lists 26.4 million rows; the paper notes that prompt counts can derive from the same audio, so row counts are not unique recordings. The official BEANS-Zero card reports 91,965 examples in a single test split and says the composite is not intended for commercial use; it documents a license field per sample. The live Hugging Face tree lists 50.5 GB, while the companion GitHub README says about 100 GB for a complete non-streaming download. Its tasks cover classification, detection, call-type/lifestage and captioning, not receiver comprehension or communication meaning.

No single mapped site

Observation dates not established here

Evidence, location & limits

The ICLR results are benchmark-specific, and Talk2Nature has not independently reproduced them. The follow-up is a NeurIPS 2025 workshop paper/preprint, not a Talk2Nature validation; its authors state that the unseen-family closed-set result is limited in scope. Current official documentation says the v1.1 demo was updated in April 2026, while its Quick Start says performance is strongest for birds, especially in North America and Western Europe; calls above about 8 kHz (including bat calls) are outside the model frequency range; tropical regions are harder; wrong-taxon prompts are not reliably rejected; and emotion interpretation or translation beyond call-type prediction is unsupported. Several other prompted tasks are explicitly experimental. The model card identifies CC BY-NC-SA 4.0 weights with Llama 3.1 terms also applicable. The 26.4M-row training dataset displays a generic “other” license and its row-level license values differ. BEANS-Zero also has a generic Hub-level “other” value, but its card explicitly says it is a composite, exposes a per-sample license field and is not intended for commercial use. No sample-level rights were inspected. Its live Hub page lists 50.5 GB while its official code README estimates about 100 GB; the size discrepancy is unresolved. The NatureLM code repository and merging-code repository declare MIT, but that does not clear weights or datasets. At the pinned BEANS-Zero code commit, the root inventory has no LICENSE and pyproject.toml declares no license; reuse permission remains unresolved. No files were acquired, and no model or code was run.

Reviewed the ICLR 2025 proceedings abstract, architecture/training sections, BEANS-Zero tasks and evaluation, Tables 3–6, the BirdNET/Perch comparison caveat, conclusion and risk discussion. Reviewed Sections 2–4 and limitations of the 11-page arXiv v2 follow-up identified by its authors as a NeurIPS 2025 workshop paper. Checked the official overview/Quick Start, the Hugging Face model and dataset metadata, and the official code/data repository inventories and license files described in the source records. No dataset files, code or model weights were acquired, no inference was run and no results were reproduced. On October 13, also reviewed the BEANS-Zero dataset card at its visible commit, its live file inventory, and the official evaluation-code repository at pinned main commit 31d4487ee6452ae6c31853d45fd38b7d4150372d, including the project metadata. No dataset shards, sample rows, code or dependencies were downloaded or executed.

This note covers a tool, benchmark, method or claim review rather than one verified observation site.

Location metadata reviewed: 2026-10-03. Map points are approximate locators, not sample coordinates.

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2026Dataset article

Birds / Dataset and detection-method review

Nocturnal bird migration data localizes calls, not communication meaning ↗

The NBM resource is a useful example of volunteer-supported passive-acoustic annotation for a narrow bird-monitoring task. Its training collection is reported as 2,077 audio files, 13,359 time-and-frequency vocalization annotations, about 38 hours and 117 species; 883 training files came from Xeno-Canto. The paper reports a two-stage object detector and mAP@IoU0.5 of 0.67 on 45 species. Those outputs localize and classify calls on spectrograms. They do not label observed behavior, receiver response, intention or meaning. The paper's test-set description says six XC files per species, 270 total, selected from recordings uploaded after the training collection; its detection-evaluation section instead says 271 recordings. Resolve that one-file discrepancy before attempting reproduction.

No single mapped site

Observation dates not established here

Evidence, location & limits

The upload-time cutoff does not by itself establish separation by bird, recording site, uploader or other biological grouping. The baseline is author-reported and not independently reproduced. The article's comparison with BirdNET includes a different multi-label classification task, so scores should not be treated as a direct translation comparison. Zenodo states that NBM-Orig audio is CC BY 4.0 while XC-origin recordings keep their individual licenses; it also describes date/location metadata for almost all XC files. Annotation and metadata terms need separate confirmation, and any future location handling must minimize precise wildlife sites. The GitHub README points to Zenodo v3 (DOI 17544346) while the paper and current dataset record point to v4 (DOI 17573913). The repository displays a model_weights folder, but no license file was visible in the inspected root; the linked 128 MB software record's license field and archive contents were not verified. Code, weights, paper text, recordings and metadata therefore remain separate rights questions.

Reviewed the Scientific Data article's abstract, methods, data records, technical validation, model-evaluation description, data/code availability and article-rights statement; the current Zenodo v4 landing page and file list; the linked GitHub repository root and README; and the Zenodo software-record page. No dataset file, metadata CSV, audio, code archive or model weight was downloaded, opened, installed or executed. No result was independently reproduced.

This is a methods and resource review. The Zenodo description notes source-dependent geography and location metadata, but no coordinate file was opened or copied and no site is mapped.

Location metadata reviewed: 2026-10-25. Map points are approximate locators, not sample coordinates.

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2026Scientific Data Early Article and Zenodo dataset v2

Birds of northeastern North America / Strongly annotated passive-acoustic dataset

A large forest-bird soundscape set can test detection, not conversation ↗

The latest Zenodo v2 record describes 1,302 ten-minute forest soundscapes—about 217 hours—from 104 sites in four northeastern U.S. parks, with approximately 183,000 vocalization annotations. The version-pinned site_metadata.csv has 104 unique sites; recording_metadata.csv independently has the same 104 site IDs. Its site counts are ACAD 31, HBEF 24, KAWW 24 and MABI 25. The landing-page opening summary says KAWW has 20 sites, so the headline total is supported while that park breakdown remains inconsistent. The v2 README says 88 bird species, matching the publisher abstract and earlier v1; however, species_metadata.csv has 96 rows marked IsBird=Yes, all with nonzero annotation counts totaling 182,633. The species-count discrepancy therefore remains unresolved. This is a promising field-acoustics resource for event detection, localization and species classification, not a communication dataset.

Location awaiting review

Observation dates not established here

Evidence, location & limits

The annotations identify vocalization events and species, not individual callers, observed social behavior, receiver response or call meaning. The site total of 104 is corroborated by two metadata tables and the README, but the landing page lists KAWW as 20 while site_metadata.csv has 24. The v2 README/publisher abstract say 88 bird species while the species table marks 96 rows as birds; the annotation total for those rows is 182,633, and the discrepancy is unresolved. The current record says annotators may combine consecutive same-species calls when pauses are under two seconds, and faint or unidentifiable events may remain unlabeled. Sampling differs by subset: SIMR is tied to point-count surveys across sites, while ACAD and MABI use selected clear mornings during peak breeding season. These choices matter for event recall, class balance and site/time holdouts. The archive is concentrated in four parks in one region and does not establish performance on household parrots, phones or other taxa. Site coordinates are present in the metadata; no values were published or mapped. The dataset record declares CC BY 4.0, while the article text is CC BY-NC-ND 4.0; neither statement is an independent audit of every component or source-media provenance.

Reviewed the publisher title, abstract, publication notice and rights statement; the full article and supplement remain unreviewed. Read the official Zenodo v1/v2 record descriptions and complete v2 API manifest. Acquired only ReadMe.txt, site_metadata.csv, recording_metadata.csv and species_metadata.csv from v2. Verified provider MD5 declarations against bytes and recorded local SHA-256 values in an ignored receipt. The aggregate script emits no raw site IDs or coordinates. No recordings, annotation rows, code or models were acquired or run.

The study covers four northeastern U.S. parks and the public record includes site coordinates. No coordinates were copied or mapped; assess species and site sensitivity before any atlas entry.

Location metadata reviewed: 2026-10-23. Map points are approximate locators, not sample coordinates.

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2026arXiv v1, September 2026; the authors’ pinned README lists it as a NeurIPS 2026 Datasets & Evaluations Spotlight.

Bottlenose dolphins / Longitudinal bioacoustic benchmark

OpenWhistle: a strong dolphin sound benchmark, not a translator ↗

OpenWhistle is a timely single-species research candidate: its authors report about 114 hours of 96 kHz recordings collected over five years from five known dolphins at Dolphin Reef in Eilat, plus 8,354 expert-annotated whistles in 10 categories. The authors describe the site as a semi-natural marine area open to the Gulf of Aqaba and say human–dolphin interactions are dolphin-initiated and voluntary. The official Hugging Face cards list 31,780 unlabeled audio segments and a separate labeled set split into 5,848 training, 1,253 validation and 1,253 test examples. Published tasks detect whistle presence or classify whistle type. These are upstream descriptions, not a Talk2Nature reproduction.

Eilat, Israel

Recordings span 2019–2024

Evidence, location & limits

All recordings come from one site and a pod of five; the paper says results need validation on independent groups. Its labels cover whistle categories, including individual signature-whistle types, not experimentally established message meanings or receiver understanding. The main evaluation holds out recording sessions, not whole animals, and the expert labels cover only a short part of the five-year span; the labels are highly imbalanced. The official repository README and LICENSE-DATA declare CC BY 4.0 for the dataset releases, while the pinned Hugging Face dataset-card fields are null; code terms were not visible in the repository root and the separate model card says its license is unspecified. No files are admitted or acquired.

Reviewed the arXiv v1 paper data-collection, annotation, benchmark, evaluation, limitations and ethics sections; revision-pinned Hugging Face pretraining and classification dataset cards and file inventories; the separate revision-pinned model card; and the pinned official OpenWhistle README (source 411) and LICENSE-DATA (source 412) at commit ad715bb3cf24c36f85b3c3c8159077548134a39e. The repository README applies CC BY 4.0 to its four named dataset releases and LICENSE-DATA grants those terms; the dataset-card API license fields remain null. No audio, labels, code or weights were acquired or run. No independent specialist reviewed this note.

The primary study names Dolphin Reef in Eilat and describes a semi-natural coastal site open to the Gulf of Aqaba, with human–dolphin interactions described as dolphin-initiated and voluntary. The atlas displays only a rounded Eilat city reference; it does not publish the facility, hydrophone deployments, dolphin positions or study boundary.

  • Eilat, Israel · Study region

    The paper identifies Dolphin Reef at Eilat. The map anchor uses the GeoNames populated-place point rounded to one decimal degree; it is a regional reference, not a facility, recorder or animal coordinate.

    Location source ↗ · arXiv v1, Section 3 Data Collection and Figure 1 caption (site description)

Observation-date source ↗ · Section 4.2, Scale and Coverage

Location metadata reviewed: 2026-10-25. Map points are approximate locators, not sample coordinates.

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2026arXiv v1 posted July 10, 2026; official code README lists ECCV 2026

Mammals / Text-to-video retrieval benchmark

Natural-language prompts can retrieve annotated wildlife videos ↗

Prompting-MammAlps tests whether natural-language queries can retrieve camera-trap videos whose structured, visible-behavior annotations satisfy each query. The 135 ecology-informed queries include multi-attribute, multi-animal and temporal relations. This is a text interface to a video-label schema, not translation of animal signals or a two-way communication system.

Swiss National Park region, Switzerland

Field seasons: 2023 and 2024

Evidence, location & limits

The benchmark is video-only and covers seven Alpine mammal species, three sites in one Swiss national park, 15 camera views and two non-overlapping seasons (2023 and 2024); it is not a parrot or audio benchmark. The published split is by acquisition day, not verified individual identity, and cross-season distribution shift is reported. Query/video matches are built from ground-truth annotations; independent label-reliability review was not performed here. The authors report set-based F1: SALMA plus the query agent scores 0.34 across all 135 queries and 0.37 on the 113 without video-comparison prompts; zero-shot InternVideo2.0 scores 0.18 only on those 113. The commonly repeated 34% vs 18% figures therefore do not compare identical query sets. An oracle query agent on ground-truth annotations scores 0.87, so retrieval quality depends strongly on the upstream perception/annotation layer. No result was independently reproduced. Dataset terms are CC BY-NC 4.0, the code repository has no declared license, and no SALMA checkpoint release was found.

Targeted review of arXiv v1 sections 1, 3.1–3.2, 4–7 and supplement S1.1–S1.3; official EPFL project page; pinned Hugging Face dataset card; official GitHub README, pyproject, SALMA README, repository root inventory and checkpoint issue. No dataset, code, article or model weights were acquired; no code or result was run.

The project places camera traps at three sites in the Swiss National Park but does not publish site coordinates. The atlas reuses the existing coarse park-region marker; it is not a camera location.

  • Swiss National Park region, Switzerland · Study region

    The official page identifies three camera-trap sites in the Swiss National Park. Only the existing approximate regional marker is used; exact sites are not inferred.

    Location source ↗ · Official EPFL project page, MammAlps-S2 overview

Observation-date source ↗ · Pinned dataset card, dataset description and recording-period fields

Location metadata reviewed: 2026-10-11. Map points are approximate locators, not sample coordinates.

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2026Versioned dataset and preprint reviewed

Birds / Strong-label dataset and baseline review

Strong bird-call labels can test detectors, not communication ↗

PteroSet v5 is a useful time-frequency bird-event detection comparator: its released record describes 563 time-lapse WAVs totaling 73.62 hours, 15,372 expert annotations and 6,702 species-level determinations across 168 species. It offers a binary bird-presence baseline. That target is whether a window overlaps an annotated bird vocalization, not what a bird is communicating. The dataset supplies no independently observed behavior, interaction, recipient response or communicative-meaning labels.

Pivijay study region, Magdalena, Colombia · Puerto Asís study region, Putumayo, Colombia

Five monitoring seasons from April 2023 to July 2025

Evidence, location & limits

The five reported projects cover two named landscapes: MAP1 at Pivijay, Magdalena, and PPA1–PPA4 at Puerto Asís, Putumayo. Therefore only the MAP1 test fold holds out a distinct named landscape; the four PPA folds test other projects while the same broad landscape remains in training. This is an audit inference from the paper's project/site table, not a reanalysis. Each time-lapse file concatenates 10-second clips sampled 30 minutes apart; its timeline is discontinuous, and the v5 segment manifest is needed to recover original segment mapping. Annotators retained clearly identifiable signals and excluded faint or ambiguous calls. The paper labels windows with no overlapping annotation as negative, which should not be equated automatically with confirmed biological silence. The pinned code declares Zenodo record 21829388 and its README lists metadata_v2.csv and segment_manifest.csv. All 12 names, sizes and provider MD5 strings in the exact v5 API manifest match this catalog, but no file bytes were hashed. A bounded 4,096-byte HTTP 206 range request streamed metadata_v2.csv; only the header was inspected, confirming location_id, recorder_id, date_recorded, latitude and longitude fields. Row values were not reviewed or saved. The README downloader resolves the concept DOI's latest version by default, and its manifest example still names metadata.csv. The pinned data_reader.py has a breakpoint() in initialization and emits latitude/longitude into annotation records. Treat code-to-file compatibility, data joins and location sensitivity as unresolved; precise wildlife coordinates require a documented handling decision before any reuse or public mapping.

Reviewed arXiv v2 methods, annotation design, dataset schema, technical validation, project-split evaluation, discussion and license; the exact Zenodo v5 API manifest; and pinned repository source. All 12 provider-reported filenames, byte sizes and MD5 strings match this catalog; no dataset file bytes were hashed. A 4,096-byte HTTP 206 range of metadata_v2.csv was streamed transiently and only its header line inspected; it names location_id, recorder_id, date_recorded, latitude and longitude. Raw GitHub source files were fetched read-only into a transient filter for static review; none were saved to the project, installed or executed. No data-row values were reviewed or saved; no annotation rows, manifest rows, audio or checkpoints were inspected. No independent reproduction or specialist review occurred.

The paper names two study municipalities and shows their deployment regions. The atlas uses rounded municipal reference points only, not recorder or sample coordinates. No dataset rows or species-specific locations were retrieved, reviewed or published; the markers do not imply that every recording is at the town center.

  • Pivijay study region, Magdalena, Colombia · Study region

    The primary paper names Pivijay in Magdalena as a study site. The marker uses the GeoNames municipal reference point rounded to one decimal degree; it is an editorial region anchor, not a recorder coordinate.

    Location source ↗ · Methods, Data Collection and Table 1: Magdalena–Pivijay / MAP1; Figure 2 regional map
  • Puerto Asís study region, Putumayo, Colombia · Study region

    The primary paper names Puerto Asís in Putumayo as a study site. The marker uses the GeoNames municipal reference point rounded to one decimal degree; it is an editorial region anchor, not a recorder coordinate.

    Location source ↗ · Methods, Data Collection and Table 1: Putumayo–Puerto Asís / PPA1–PPA4; Figure 2 regional map

Observation-date source ↗ · Methods, Data Collection and Table 1: five project recording periods

Location metadata reviewed: 2026-10-25. Map points are approximate locators, not sample coordinates.

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2026alp-data 1.11.1, released July 31, 2026

Cross-species bioacoustics / Dataset access software

A shared bioacoustic dataset loader, with rights kept separate ↗

The project describes a shared Python interface for more than 35 bioacoustic datasets across several animal groups, with streaming, configurable transforms and per-dataset metadata. Its README explicitly leaves each dataset under its original creator’s terms.

No single mapped site

Observation dates not established here

Evidence, location & limits

The software license covers alp-data code, not the datasets, recordings, annotations, model weights or dependencies. Dataset-specific rights, consent, privacy, provenance, labels and animal/session splits were not examined. The repository overview is mutable and its “35+” count is the project’s own current claim. This review does not establish a Talk2Nature integration or a benchmark of communication or meaning.

Read the public alp-data README and project configuration and reviewed the exact PyPI 1.11.1 release page and MIT license declaration. No package, dependencies or datasets were downloaded, installed or run. No individual dataset entry was cleared for use.

This note reviews a multi-dataset software tool, not a location-specific animal study.

Location metadata reviewed: 2026-10-13. Map points are approximate locators, not sample coordinates.

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2026arXiv:2610.07107v1, submitted 5 October 2026; preprint

Cross-species: rock hyrax, zebra finch, little owl and infant marmoset / Cross-species representation-transfer study

Speech models carry animal vocal patterns—not animal meanings ↗

Arias-Vergara et al. compare 15 frozen speech, speaker-verification, animal-bioacoustic and general-audio encoders on caller identification in four species and call-type classification in three. The strongest speech-pretrained representation outperforms animal-pretrained representations in three of seven task–dataset comparisons; the other four differences are not statistically significant. Controlled comparisons find no systematic advantage from multilingual coverage or animal-domain pretraining, and useful encoder layers vary with architecture. This shows that some acoustic caller and call-type information transfers across domains; the study does not predict independently observed behavior or test message meaning.

Location awaiting review

Observation dates not established here

Evidence, location & limits

Caller-identification folds were grouped by recording session, but individuals with fewer than three sessions had their largest session split into contiguous blocks so the same caller appeared in different folds. This applied to 2/16 hyraxes, 13/33 zebra finches and all 16 little owls. The paper's session-independent sensitivity analysis reports a 0.19–0.37 UAR drop for little owls; zebra-finch UAR was 0.44 for the session-independent birds versus 0.81 for block-split birds. Therefore some caller-ID results do not estimate performance on wholly unseen individuals. The separate call-type task holds callers out, but its labels are vocal categories, not meanings. The study uses four taxa, historical source corpora and frozen encoders; fine-tuning could change the results. Source-audio, code, dependency and model-weight rights are separate and were not assessed, and results were not independently reproduced.

Reviewed the complete arXiv v1 HTML, including dataset descriptions, encoder families, caller-identification and call-type tasks, fold construction, result tables, the session-independent sensitivity analysis and conclusion. Confirmed the submitted version/date and CC BY 4.0 manuscript license on the arXiv record. No paper-linked code or data release was identified in the reviewed record/full text; this is not an exhaustive search. No audio, dataset files, code or weights were acquired or run. The arXiv record does not establish publication or peer-review status.

The preprint combines four reused corpora. Review the original source studies and location precision before assigning map regions; no places are mapped in this note.

Location metadata reviewed: 2026-10-12. Map points are approximate locators, not sample coordinates.

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2026Dataset-card revision and code sample reviewed

Zebra finches / Multimodal caller-attribution dataset audit

V33DA: a zebra-finch caller benchmark, not a translation model ↗

V33DA is a caller-attribution task: given an already-detected zebra-finch vocalization and the birds visible at that moment, identify the bird that produced it. The pinned card reports 33,625 verified events from 10 physically distinct birds across three experiments, with synchronized audio, video and pose; caller labels derive from on-body accelerometer vibration. In the experiment-held-out split, all 5,829 BP05 events from three callers (brown, purple, yellow) form the test set. That is a genuine unseen-bird group according to the card, but not a clean explanation of generalization because several cohort conditions shift together. This is an engineering benchmark candidate, not a communication-meaning result.

Location awaiting review

Observation dates not established here

Evidence, location & limits

Caller identity is not behavior, addressee, receiver response, intent or meaning. The full release comes from one aviary and recording geometry and excludes overlapping calls; 10 physical birds, not 33,625 events, are the independent animals. The BP05 test also changes experiment, group, year/date and cage; treat its outcome only as a bundled shift test. The session-disjoint split retains the same birds. The pinned GitHub tree lacks the v33da/data package imported by scripts/train.py, including dataset, feature and split modules; the split-preparation script writes date-based session and held-out-experiment labels only. The README advertises leave-one-bird-out, but that path and the trainer cannot be independently reproduced from this snapshot. Three small text files have verified hashes, but the sample Parquet says 100 rows while split JSON reports full-release totals; no sample-specific split or Parquet join has been verified. BP05 metadata names brown, purple, yellow and red, while the card and caller-count table list three callers. Red has no event-count group; whether it is a visible non-caller candidate or stale metadata is unknown. Candidate-set cardinality and chance baseline are therefore unknown. No media or Parquet bytes were inspected. The separate V33DA++ companion adds overlap and padded context buckets but still uses the same ten birds and supplies no independently observed receiver behavior; see the companion note.

Reviewed the pinned V33DA dataset card, code/sample README, split-preparation script, training entry point and read-only Git-tree inventory. Fetched the sample README.md, metadata.json and split_summary.json over verified TLS; computed in-memory SHA-256 values matched the pinned Git blob IDs. See the manifest for checksums. No text copies were retained. No Parquet, audio, accelerometer, video, calibration, executable code or model bytes were acquired or run. Maintainer metadata review only; no specialist review. A follow-up static import/tree check confirmed that scripts/train.py imports v33da.data modules absent from the pinned public repository tree; the split-preparation script materializes only session and held-out-experiment assignments. No code was run.

Location has not yet been checked in primary sources.

Location metadata reviewed: Not yet reviewed. Map points are approximate locators, not sample coordinates.

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2026Pinned companion-dataset card reviewed

Zebra finches / Overlapping-call and context dataset review

V33DA++ adds overlapping calls and context, not meaning ↗

V33DA++ is a companion to the physically verified zebra-finch caller-attribution dataset. Its card describes 3,784 reviewer-verified overlapping-call rows, 33,625 single-vocalizer events with plus/minus two seconds of context, and 3,784 overlapping events with the same context. The overlap_padded bucket repeats the overlap events with padding, so the provider's 41,193-row total is not 41,193 unique events. The recordings and all ten birds are the same as V33DA. The card suggests source separation, vocal-activity detection, audio-visual synchronization and active-speaker detection; caller evidence is anchored by on-body accelerometers, which are an oracle label source rather than an admissible predictor.

Location awaiting review

Observation dates not established here

Evidence, location & limits

This is a signal-processing and attribution resource, not a communication or interaction dataset: the reviewed card does not supply independently observed behavior, addressee, receiver response, intent or call meaning labels. It adds temporal context and overlapping events, not new animals or an independent cohort; results remain bounded to the same ten finches and controlled recording setup. The provider lists 167 GB for the release. The linked code repository's current main is still commit e988b985e38909b40b2705cdad994e3d9307181e; scripts/train.py imports v33da.data modules absent from that tree, while the split-preparation script writes session and held-out-experiment date assignments but no auditable leave-one-bird-out folds. The code path is not reproducible from the reviewed snapshot. The dataset card declares CC BY 4.0, while preprocessing tools and code are separately licensed; these declarations are not independent clearance.

Reviewed the V33DA++ Hugging Face dataset card at immutable revision bb31f73e2191278dd4965af01502c42380cd597d, including bucket counts, data-field definitions, suggested tasks and rights notice; checked the provider API revision/date/license metadata. Rechecked the linked GitHub repository's current main revision e988b985e38909b40b2705cdad994e3d9307181e, its training imports and split-preparation script as public text, and the Git tree. No dataset files, media, Parquet, code or weights were acquired or run; no specialist review occurred.

Location has not yet been checked in primary sources.

Location metadata reviewed: Not yet reviewed. Map points are approximate locators, not sample coordinates.

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2026Journal paper and latest Zenodo archive version

Birds / Global multi-site passive acoustic dataset

WABAD maps bird calls at scale; it does not map meaning ↗

WABAD is a promising large-scale bird-acoustic reference. The publisher reports 5,047 minutes of audio with 91,931 species-level annotated vocalizations from 1,192 species, 72 recording sites, 29 locations and 13 biomes. The latest Zenodo record lists 21.3 GB. This scale could help researchers test whether a sound model detects and identifies bird vocalizations across sites. The reviewed task description concerns vocalization occurrence and species labels; it does not test a receiver's response or what a call means.

No single mapped site

Observation dates not established here

Evidence, location & limits

The journal abstract states that the dataset is CC BY 4.0, but the current Zenodo v4 record states CC BY-NC 4.0. The archive's version history and publisher data statement point to different versions; the reuse discrepancy is unresolved. Source-recording provenance and per-file conditions were not audited. We did not inspect the underlying audio, annotations, metadata, label quality, grouping keys or any train/test split. WABAD therefore remains a metadata-only research candidate, not an admitted training set or a Talk2Nature benchmark.

Reviewed the publisher abstract and data-availability statement, plus the official current Zenodo v4 record, version history and file inventory. The full article body, supporting-information PDF, archive contents, metadata schema, labels, evaluation split and code were not inspected; no data or models were acquired or run.

This global acoustic compilation has no single field region for the literature atlas. The publisher reports 72 recording sites across 29 recording locations, mainly countries, and 13 biomes. This review used only those broad coverage statements; the supporting metadata and individual locations were not inspected. Do not add a world centroid or publish record-level coordinates.

Location metadata reviewed: 2026-10-25. Map points are approximate locators, not sample coordinates.

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2026Source year

Marine mammals / Observation

A birth, a group, and changing vocal styles ↗

A reported sperm whale birth connects changing vocal styles with an observed social event.

Waters off Dominica

Observed 8 July 2023

Evidence, location & limits

A single event offers a valuable observation, but cannot establish a general call dictionary or causal interpretation.

Primary study summary in founding audit; full-text methods review pending.

The authors describe an observed birth off Dominica. The map uses a broad offshore locator.

  • Waters off Dominica · Study region

    The authors describe an observed birth off Dominica. The map uses a broad offshore locator.

    Location source ↗ · Results: Sperm whale birth

Observation-date source ↗ · Results: Sperm whale birth, opening paragraph

Location metadata reviewed: 2026-10-03. Map points are approximate locators, not sample coordinates.

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2026Publication year

Yellow-naped amazon (Amazona auropalliata) / Wild vocal-sequence study and dataset review

Wild parrot duet structure: sequence patterns, not decoded meaning ↗

The study examined warble duets from 13 mated pairs of wild yellow-naped amazons in north-west Costa Rica during 2006–2008. The authors report creating 1,136 duet files; 52 high-quality warble duets from 12 pairs were used in the syntax analysis. They report recurring call types, flexible ordering, 19 positive and four negative call-type associations, while most complete duet sequences were unique. This is evidence of measurable sequence structure in this study, not a translated conversation.

Costa Rica

Field recordings: 2006–2008

Evidence, location & limits

Analysis-specific sample counts differ: the initial lexicon is described as 469 calls from 54 duets and 13 pairs, while syntax analysis used 52 duets from 12 pairs. Methods describe 36 multi-example call types plus 11 one-time forms grouped as “Unknown”; results report 36 call types and three variants. The reported 92% repeatability came from two supervised undergraduate scorers scoring 15 hidden duets three times; it is not independent external validation. Calls could overlap and be difficult to segment, and sex assignment relied on established duet-order rules. Quadratic discriminant analysis and naive Bayes did not classify the full call-type set; pairwise naive Bayes separated only two similar types. Syntax/collocation is not semantic decoding: the authors state that they cannot confidently assign meanings to calls, collocates or duets and discuss possible territory-defense functions. Recordings used a tripod-mounted directional microphone and video, not ordinary phone capture. The species is described as Critically Endangered, so this atlas uses country-scale geography only. Dryad lists a small measurements workbook, not raw audio; the publisher separately lists a WAV and two DOCX supplements whose terms and contents were not reviewed. The specific article reuse license was not established. No Talk2Nature specialist review, independent replication or model result exists.

Reviewed the publisher article HTML for its abstract, sites and recordings, call categorization, acoustic analyses, syntax methods and results, discussion, data-availability statement and supplementary-file inventory. Reviewed the full Dryad dataset landing page and README description and Dryad’s current reuse-rights guide. The workbook and publisher supplements were not downloaded or inspected; no published analysis was reproduced.

The article methods place the wild study in north-west Costa Rica. Because the focal species is Critically Endangered, the public atlas uses a country-scale point and omits the named property and any exact site.

  • Costa Rica · Study region

    The publisher article identifies north-west Costa Rica as the field region. The public point is generalized to the country; no exact property or recording coordinates are published.

    Location source ↗ · Material and methods, Sites and recordings

Observation-date source ↗ · Material and methods, Sites and recordings

Location metadata reviewed: 2026-10-12. Map points are approximate locators, not sample coordinates.

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2026Peer-reviewed Machine Learning and Knowledge Extraction article

Zebra finch (Taeniopygia guttata) / Bioacoustic classification study

A zebra-finch classifier is a baseline, not a translator ↗

The 2026 article introduces multi-task classification using frozen BirdAVES embeddings: it predicts 11 supplied call-type labels and six behavior-derived functional/semantic categories as an auxiliary target. The authors report a best call-type result of 87.11% accuracy and 82.76% macro-F1, versus 84.86% and 78.89% for their single-task call-type baseline. They report one predefined 70:15:15 split by bird. These are author-reported results, not reproduced by Talk2Nature. The semantic categories are supervised labels from an existing taxonomy; they are not discovered meanings or an open-ended translation.

Location awaiting review

Observation dates not established here

Evidence, location & limits

The paper says 50 birds, while Elie & Theunissen (2016) report 45 birds and 8,136 segmented calls from 3,405 bouts; the Figshare record lists 3,433 WAV files. The 2026 paper says each input is a single isolated vocalization, whereas the Figshare record warns that files may contain a vocalization or sequence. These may reflect different processing or dataset versions, but the relationship and unitization are unresolved. The evaluation uses one split; only 3 DistressCall recordings appear in its test partition. The colony recordings do not establish performance on ordinary homes or field soundscapes. The study does not test receiver responses or align model errors with the 2025 behavioral-trial confusion data. Exact BirdAVES variant, weight terms, article authors’ experiment-code location and supplementary contents remain unresolved. AVEX declares MIT for its library code, which is a separate component and does not license pretrained weights or data. Dataset, article, code and model-weight rights are separate; the dataset record declares CC BY 4.0, the journal specifies CC BY 4.0 for the article, and no data/model asset has been cleared or admitted. This study is important prior art for using a behavior-derived semantic hierarchy as model supervision. It does not report a direct item-level comparison between its predictions and the receiver birds' measured confusion responses from the 2025 experiment.

Reviewed the publisher-hosted article's abstract, data/methods, bird-level partition, evaluation, results, limitations, conclusion and data statement on October 24, plus the article-specific version history and AVEX documentation. On October 25, rechecked the publisher-indexed title, framing and result excerpt; the direct MDPI page returned HTTP 429 during this refresh, so no new full-text review is claimed. The official Figshare v1 and Zenodo v1/v3 APIs were refreshed over verified TLS. No archive, code, audio or BirdAVES weights were acquired or run, and no model result was reproduced.

Location has not yet been checked in primary sources.

Location metadata reviewed: Not yet reviewed. Map points are approximate locators, not sample coordinates.

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2026Peer-reviewed Applied Sciences article

Zebra finch (Taeniopygia guttata) / Bioacoustic annotation workflow study

Human-guided clustering helps label zebra-finch calls, not decode meaning ↗

The article reports 1,454 randomly selected 44.1 kHz vocalizations from three isolated zebra finches recorded between 55 and 136 days post-hatch, with independent manual clusters from three experts for each bird. Mean ARI/NMI were 0.78/0.83 for expert-to-expert manual agreement, 0.73/0.81 for human-refined clusters and 0.62/0.71 for fully automated Whisper-based clustering. The authors also report about 90 minutes per bird for manual clustering versus 45 minutes for semi-automated proofreading. These are published results, not Talk2Nature reproductions. The method suggests that model proposals plus human correction may scale annotation while preserving expert review; its categories are acoustic clusters, not validated call meanings.

Location awaiting review

Observation dates not established here

Evidence, location & limits

The article's 80/20 split randomly holds out vocalizations within each bird-and-annotator combination and evaluates nine such combinations from only three birds; it does not test unseen-bird or cross-species generalization. The study analyzes acoustic similarity rather than receiver response, social context or semantics. Time savings and agreement were reported on this small task, not independently verified. The Zenodo API shows that paper-cited v1 record 18156254 and v2 record 22657329 share conceptrecid 18156253, with different archive sizes and checksums. The v2 record note also names 18100613 as an earlier publication, but that DOI has a different conceptrecid (18100612); its relationship to the article data is unresolved. The Zenodo APIs declare CC BY 4.0, but the paper acknowledges permission to use a subset from an external dataset, so source-audio rights and exact archive contents require reconciliation. The code repository is MIT at the pinned revision. The model card displays Apache-2.0 for WhisperSeg, while the pinned model API reports no license value; the paper's Whisper embedding model revision is not identified. No independent reviewer or reproduction exists.

Reviewed the full 15-page article text, including methods, reported ARI/NMI results, time comparison, split design, discussion, acknowledgments and data statement. Queried exact Zenodo record and version APIs for 18156254, 22657329 and 18100613; read the pinned GitHub README, root requirements and MIT license; and compared the exact WhisperSeg model page with pinned revision metadata. No data archive, code, model weights or supplement was downloaded or executed. Figures were not visually reanalyzed and no independent specialist reviewed this note.

Location has not yet been checked in primary sources.

Location metadata reviewed: Not yet reviewed. Map points are approximate locators, not sample coordinates.

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2026bioRxiv preprint v3; not peer reviewed

Zebra finch (Taeniopygia guttata) / Vocal interaction and model study

Zebra-finches exchange calls with an interactive audio model ↗

Female zebra-finches changed the timing and acoustic structure of their calls during exchanges. The paper reports more than 1,000 hours of recordings and analyzes 834,309 calls from 21 birds across 40 pair interactions in Methods; one Results passage says 20 birds, so the count is inconsistent. The authors define a response as a first call within one second after a partner's call ends. They report responses after about 44% of partner calls, compared with an estimated 10% chance level, and describe birds responding to live sounds from an interactive audio model in a controlled lab study. The model predicts patterns such as which bird may call next and when; this is evidence about acoustic timing and contingency, not a translation of calls into human language.

Location awaiting review

Observation dates not established here

Evidence, location & limits

This is a preprint, not peer-reviewed evidence. A Results passage reports n=20 while Methods reports 21. The paper describes a pair-level split with 34 training pairs and pairs 35–40 for validation/evaluation, but the reviewed article does not establish that individual birds are disjoint across pairs. The response window is an operational timing rule, not a semantic label. The separate Zenodo data record (23047646) lists four raw example tracks plus processed-data and detector archives, not a verified copy of the full study corpus. Its API license field says CC BY-SA 4.0 while its description says the data are CC BY-NC; this conflict leaves reuse unresolved. The separate 26.6 GB code/model ZIP (23024019) remains opaque at the internal-file level. Supplemental analyses and independent reanalysis were not reviewed.

Reviewed the full bioRxiv v3 main text, including Results, Discussion, Methods 1.1–1.7, data-availability statement and posted license, plus official Zenodo landing-page/API metadata for records 23024019 and 23047646. A metadata-only refresh of the official bioRxiv v3 page on October 24 confirmed its current title and that the title and abstract were updated; the full text was not re-reviewed in this refresh. The code/model record lists one ZIP; the separate data record lists 11 files, including four raw example recordings and processed archives. No archive internals, supplemental analyses or media payloads were downloaded or inspected; no code, weights or data were run; no independent specialist reviewed this assessment.

Location has not yet been checked in primary sources.

Location metadata reviewed: Not yet reviewed. Map points are approximate locators, not sample coordinates.

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THE NEXT LAYER / OBSERVATIONS OF OUR OWN

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built carefully.

The app already helps you record, save and compare moments on your device. Publishing observations on this atlas needs reviewed consent, a study protocol and location protection. A private home or a sensitive animal site should never become a public pin by accident.

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Versioned records link to the same notes as our evidence graph. Locations are editorial display points with source passages and precision labels. This file is not raw observation data or a training dataset.