Localizing Bowhead whales in the Chukchi Sea using asynchronous hydrophones
Bibliographic record
Abstract
This paper localizes bowhead whales using Bayesian inversion of the modal dispersion of whale calls recorded on asynchronous hydrophones in the northeastern Chukchi Sea, Alaska. A cluster of seven asynchronous ocean-bottom hydrophones (OBH), separated by up to 7.5 km, recorded low-frequency bowhead whale calls as the whales migrated through the Chukchi Sea. The calls dispersed into multiple modes after propagating through the shallow water environment. Relative mode arrival times are extracted from the recordings using a warping time-frequency analysis for nine frequency-modulated whale calls, each of which were recorded on multiple OBHs. A trans-dimensional Bayesian inversion approach is used to invert mode arrival times for the whale location in the horizontal plane, source instantaneous frequency (IF), water sound-speed profile, seabed geoacoustic parameters, relative recorder clock drifts, and residual error standard deviation, all with estimated uncertainties. A simulation study found that accurate localization results could be obtained even without accurate prior environmental knowledge. Inverting multiple calls jointly is shown to significantly reduce uncertainties for whale location, source IF, and relative clock drift. Whale location uncertainties are less than 160 m and clock drift uncertainty is less than 26 ms. This clock synchronization accuracy is sufficient for localizing other types of marine mammal calls using simpler methods (e.g., time-difference-of-arrival).
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".