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Record W4389894118 · doi:10.1038/s41597-023-02745-4

Global inventory of species categorized by known underwater sonifery

2023· article· en· W4389894118 on OpenAlexaff
Audrey Looby, Christine Erbe, Santiago Bravo, Kieran Cox, Hailey L. Davies, Lucia Di Iorio, Youenn Jézéquel, Francis Juanes, Charles W. Martin, T. Aran Mooney, Craig A. Radford, Laura K. Reynolds, Aaron N. Rice, Amalis Riera, Rodney A. Rountree, Brittnie Spriel, Jenni A. Stanley, Sarah Vela, Miles Parsons

Bibliographic record

VenueScientific Data · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsDalhousie UniversityUniversity of VictoriaSimon Fraser University
FundersVlaamse regeringFonds Wetenschappelijk OnderzoekVlaams Instituut voor de ZeeLifeWatch – Niclas Öberg FoundationRichard Lounsbery Foundation
KeywordsBiodiversityInvertebrateUnderwaterMarine lifeEcologySubspeciesMarine biodiversityEnvironmental resource managementBiologyMarine speciesGeographyEnvironmental science

Abstract

fetched live from OpenAlex

A working group from the Global Library of Underwater Biological Sounds effort collaborated with the World Register of Marine Species (WoRMS) to create an inventory of species confirmed or expected to produce sound underwater. We used several existing inventories and additional literature searches to compile a dataset categorizing scientific knowledge of sonifery for 33,462 species and subspecies across marine mammals, other tetrapods, fishes, and invertebrates. We found 729 species documented as producing active and/or passive sounds under natural conditions, with another 21,911 species deemed likely to produce sounds based on evaluated taxonomic relationships. The dataset is available on both figshare and WoRMS where it can be regularly updated as new information becomes available. The data can also be integrated with other databases (e.g., SeaLifeBase, Global Biodiversity Information Facility) to advance future research on the distribution, evolution, ecology, management, and conservation of underwater soniferous species worldwide.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.089
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0020.006
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.004

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.068
GPT teacher head0.279
Teacher spread0.211 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations31
Published2023
Admission routes1
Has abstractyes

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