Building fish sound libraries, measuring aquatic soundscapes and quantifying the effects of noise
Bibliographic record
Abstract
Sound is a fundamental component of the sensory environment of many aquatic animals. Despite its importance, the effects of noise on aquatic organisms, particularly fishes, is not well studied nor is underwater noise regulated by most legislation. As the world has got noisier, the range and intensity of underwater anthropogenic noise continues to increase. Intense short-term noise can permanently alter auditory thresholds and lead to mortality for some organisms, while long-term chronic noise such as that produced by vessels, can lead to physiological and behavioural changes. Noise pollution can also mask environmental cues, vocalizations, or dampen the ability to hear conspecifics, prey or predators. Here, I will summarize our work developing quantitative descriptions of marine soundscapes and use our results to better understand the effects of noise on sound production and behaviour of fishes. Specifically, we are quantifying the known soniferous fish species to better understand taxonomic and geographic distribution patterns; developing inexpensive, novel, long-term, field-based methodological and statistical tools to localize and automatically detect vocalizing species; and quantifying and modeling the effects of noise on fish ecology in natural systems.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".