Bowhead whale localization and environmental inversion using asynchronous hydrophones
Bibliographic record
Abstract
This paper estimates bowhead whale locations and environmental properties using Bayesian inversion of the modal dispersion of whale calls recorded on asynchronous recorders in the shallow waters of the northeastern Chukchi Sea, Alaska. Bowhead calls were recorded on a Y-shaped cluster of seven autonomous ocean-bottom hydrophones, separated by up to 9.2 km. We use a warping time-frequency analysis to obtain frequency-dependent relative mode arrival times for nine frequency-modulated whale calls. Trans-dimensional inversion is applied to invert mode arrival times for the whale location, water sound-speed profile, subbottom layering and geoacoustic parameters, source instantaneous frequency (IF), relative recorder clock drifts, and residual error standard deviation, all with estimated uncertainties. Joint inversion of multiple calls is found to substantially reduce uncertainties on whale location, source IF, and clock drifts. Estimated whale location uncertainties are 30–160 m and clock drift uncertainties are 3–26 ms. The prior and posterior probability densities for environmental parameters are used to quantify transmission loss uncertainties corresponding to different levels of environmental knowledge, with applications to computing marine-mammal sound exposure levels.
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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 | 0.002 |
| 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 | 0.001 |
| 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".