Passive acoustic localization of fish using a compact hydrophone array
Bibliographic record
Abstract
Passive acoustic monitoring of fish in their natural environment is a research field of growing interest and importance. Although many fish species are soniferous, the characterization and biological understanding of their sounds are largely unknown. Many underwater acoustic recordings contain sounds likely produced by fish, but little information can be extracted from them due to the lack of fundamental knowledge about the behaviors they represent. Deploying small hydrophone arrays can help fill some of these knowledge gaps. Passive acoustic localization using fish calls received on multiple hydrophones can be used to estimate swimming speed, calling rate of individual fish, and source level of their calls. This paper focuses on the three-dimensional localization of fish using a compact array of 6 hydrophones using both simulated and measured data. Fish sounds were detected manually on one of the hydrophones. Time difference of arrivals (TDOAs) were then defined by cross correlating the detected signal with signals from the other hydrophones. Linearized Bayesian inversion was employed to localize fish sounds from the measured TDOAs. Localization uncertainties were below 10 cm inside the hydrophone array. Simulated annealing optimization was used to define the hydrophone configuration that could provide the smallest localization uncertainties.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".