Performance of three acoustical methods for localizing whales in the Saguenay - St. Lawrence Marine Park
Bibliographic record
Abstract
Three algorithms are explored to localize fin whale calls recorded from a large-aperture hydrophone array deployed in the Saguenay - St. Lawrence Marine Park. The methods have to cope with varying sound speed in space and time, errors in time differences of arrival (TDoA) measurements in a noisy environment, and often a limited number of hydrophones having recorded a particular event. The array was composed of 5 AURAL autonomous hydrophones with a total aperture of about 40 km, coupled with 2 hydrophones from a small-aperture cabled coastal array. The autonomous hydrophones clock drifts were estimated with a level of uncertainty from timed sources and the coastal array time reference. The calls were then localized by constant-speed hyperbolic fixing, variable-speed isodiachron Monte-Carlo simulations, and a ray-tracing propagation model. The Monte-Carlo simulations generate clouds of possible localizations from the uncertainty in hydrophone positions, TDoAs and the effective horizontal sound speeds along the different source-hydrophone paths. The ray-tracing model produces a fixed grid of TDoAs which can then be consulted to find the likeliest positions of the whales. Results from the different methods are compared and their relative advantages or limitations are discussed.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".