FishSounds: A data-sharing website of global soniferous fish diversity
Bibliographic record
Abstract
Active fish sound production is geographically and taxonomically widespread—though not homogenous—among fishes, including numerous commercially and recreationally important fisheries species. Despite the ecological importance of fish sounds, their passive acoustic monitoring (PAM) applications, and extensive endeavors to document them, the field of fish bioacoustics has been historically constrained by the lack of an easily accessible, comprehensive inventory of fish sound production. To create such an inventory while simultaneously assessing the global extent of known soniferous fish species, we extracted information from almost 1000 references from the years 1874–2021 to determine that over 900 fish species have been shown to produce active (i.e., intentional) sounds. Our information is collated on the FishSounds website at FishSounds.net along with representative recordings of fish sounds that can be easily searched through and accessed by our users. FishSounds has since launched a new initiative to develop an acoustic catalog for Canadian-specific fisheries species and explore their ecological characteristics, spatial distribution, and taxonomy. The data available on FishSounds can be similarly adapted to meet other regional management needs, facilitate the application of PAM, and aid in the discovery of novel soniferous behaviors across fishes globally.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.056 | 0.029 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".