Localization and tracking of fish sounds with a 4-element underwater passive acoustic array
Bibliographic record
Abstract
Localization and tracking are parameter estimation problems. As such, estimates are evaluated by their statistical distribution, in particular, their bias (equal to the mean of estimated positions minus the true position) and variance (variability in the measured positions about their mean). A variety of methods are used to localize sounds recorded by acoustic arrays. Here, we quantitatively compare the performance of four algorithms: (1) plane-wave-front frequency-domain beamforming; (2) curved-wave-front frequency domain beamforming; (3) time-difference-of-arrivals (TDOAs) using waveform cross-correlation; and (4) TDOAs using spectrogram cross-correlation, for fish sounds generated with a controlled underwater source at various GPS-located positions around a 4-element array. The array consisted of a SoundTrap ST4300 (Ocean Instruments, Auckland, NZ) four-channel acoustic recorder, equipped with four HTI-96-MIN hydrophones (High Tech, Inc., Long Beach, MS). The hydrophones were arranged in a tetrahedral-shaped configuration with a 20-m inter-element spacing. The objective is to optimize the localization and tracking of individual soniferous fish to better understand their small-scale spawning movements and reproductive behavior. Research supported by the Office of Naval Research, the Scripps graduate department, and a Natural Sciences and Engineering Research Council of Canada (NSERC) Postgraduate Scholarship-Doctoral (PGS D-3).
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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 | 0.001 | 0.002 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".