Auditioning fish for sound production in captivity to contribute to a catalogue of known fish sounds to inform regional passive acoustic studies
Bibliographic record
Abstract
Passive Acoustic Monitoring (PAM) is increasingly used as a method to characterize underwater soundscapes and the impacts of noise on marine ecosystems. The natural sounds produced by marine mammals have been widely studied, enabling the use of PAM as an effective conservation tool. However, much less is known about fish sound production, particularly in the northeast Pacific. This lack of information makes it difficult to identify fish sounds that are present in long-term recordings and thus precluding accurate determination of fish species composition and evaluation of the effects of noise on fishes. In order to identify fish sounds in British Columbia soundscapes, we need catalogues of validated fish sounds from Pacific species. These catalogs will help in comparing validated examples to unknown sounds found in long-term autonomous recordings. Our goal is to contribute to building such catalogues and to fill knowledge gaps in fish acoustic behaviour to support studies on the impact of anthropogenic noise on Pacific fishes. We are collaborating with aquaria, ocean-based aquaculture facilities and commercial fisheries to monitor, audition, and record the acoustic behavior of captive and semi-captive fish species. Here, we will present the results of these studies.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".