Bayesian Inversion of Time-difference-of-arrival Data to Localize Bowhead whales in the Chukchi Sea
Bibliographic record
Abstract
This paper develops and illustrates a Bayesian inversion for bowhead whale localization and nonlinear uncertainty estimation using time-difference-of-arrivals (TDOA) of whale calls recorded on omni-directional asynchronous recorders in the Chukchi Sea, Alaska. A Y-shaped cluster of seven autonomous ocean-bottom hydrophones, separated by 0.5–9.2 km, was deployed for several months over which time their internal clocks drifted out of synchronization. Hundreds of recorded whale calls are manually annotated and associated between recorders. The TDOA of whale calls recorded on independent hydrophone pairs are calculated from waveform cross-correlations. These TDOA data depend on the whale locations, hydrophone locations, relative recorder clock drifts, and an effective waveguide sound speed, all of which are considered unknown parameters (with varying levels of prior information) in the Bayesian inversion. In many cases localization uncertainties are sufficiently small to allow tracking whales that vocalize repeatedly over several minutes. Estimates of clock drift rates are obtained from inversions of TDOA data over several weeks. The inversion is computationally efficient and suitable for application to large datasets of manually- or automatically detected whale calls
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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 | 0.005 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".