Identification of fish sounds in the wild using a set of portable audio‐video arrays
Bibliographic record
Abstract
Abstract Associating fish sounds to specific species and behaviours is important for making passive acoustics a viable tool for monitoring fish. While recording fish sounds in tanks can sometimes be performed, many fish do not produce sounds in captivity. Consequently, there is a need to identify fish sounds in situ and characterise these sounds under a wide variety of behaviours and habitats. We designed three portable audio‐video platforms capable of identifying species‐specific fish sounds in the wild: a large array, a mini array and a mobile array. The large and mini arrays are static autonomous platforms than can be deployed on the seafloor and record audio and video for one to two weeks. They use multichannel acoustic recorders and low‐cost video cameras mounted on PVC frames. The mobile array also uses a multichannel acoustic recorder, but mounted on a remotely operated vehicle with built‐in video, which allows remote control and real‐time positioning in response to observed fish presence. For all arrays, fish sounds were localised in three dimensions and matched to the fish positions in the video data. We deployed these three platforms at four locations off British Columbia, Canada. The large array provided the best localisation accuracy and, with its larger footprint, was well suited to habitats with a flat seafloor. The mini and mobile arrays had lower localisation accuracy but were easier to deploy, and well suited to rough/uneven seafloors. Using these arrays, we identified, for the first time, sounds from quillback rockfish Sebastes maliger , copper rockfish Sebastes caurinus and lingcod Ophiodon elongatus . In addition to measuring temporal and spectral characteristics of sounds for each species, we estimated mean source levels for lingcod and quillback rockfish sounds (115.4 and 113.5 dB re 1 μ Pa, respectively) and maximum detection ranges at two sites (between 10.5 and 33 m). All proposed array designs successfully identified fish sounds in the wild and were adapted to various budget, logistical and habitat constraints. We include here building instructions and processing scripts to help users replicate this methodology, identify more fish sounds around the world and make passive acoustics a more viable way to monitor fish.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".