Identifying fish sounds of British Columbia with an autonomous audio and video array
Bibliographic record
Abstract
Among the ∼400 marine fish species of British Columbia, only 22 have been reported to be soniferous. However, it is likely due to the lack of examination, as more species are suspected to produce sound. Here we describe how an autonomous audio and video array can identify fish sounds in situ. The array is composed of a collapsible PVC frame, an acoustic recorder, six hydrophones, and two custom-made wide-angle autonomous video cameras mounted on the top and side of the array that collect data continuously for up to 10 days. Fish sounds are automatically detected in the acoustic recordings, the time difference of arrivals between pairs of hydrophones is measured by cross correlation, and the 3-D sound-source location and its uncertainty are estimated using linearized inversion. Simulated annealing optimization was used to define the hydrophone configuration that provides the smallest localization uncertainties. The video recordings are used to assign the species of sound-producing fish localized within the array. The array was deployed at several locations around Vancouver Island and used to define the species, sound characteristics, and source levels of several fish sounds. This new information will help making passive acoustics a viable way to monitor fish in the wild.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".