Environmental characterization in the Chukchi Sea using Bayesian inversion of bowhead whale calls
Bibliographic record
Abstract
This paper estimates seabed and water-column properties of a shallow-water site in the Chukchi Sea using bowhead whale calls recorded on asynchronous ocean-bottom hydrophones. Up- and down-swept bowhead whale calls were recorded on a cluster of seven hydrophones within a 5 km radius. The calls excited multiple propagating modes, with modal dispersion controlled by environmental properties and whale-recorder range. Frequency-dependent mode arrival times for nine whale calls are inverted using a trans-dimensional (trans-D) Bayesian approach that estimates the whale locations (easting and northing) and range-independent environmental properties (sound-speed profile, water depth, and seabed geoacoustic profiles). The trans-D inversion allows the data to determine the most appropriate environmental model parameterization in terms of the number of sound-speed profile nodes and subbottom layers. The inversion also estimates each whale-call instantaneous frequency function, relative recorder clock offsets, and residual-error standard deviation, and provides uncertainty estimates for all model parameters and parameterizations. The sound-speed profile is found to be poorly resolved, but water depth and upper sediment-layer thickness and sound speed are reasonably well resolved. Model estimates and uncertainties are compared to those from separate inversions involving airgun dispersion and vessel noise data collected nearby (which also represent sources of opportunity).
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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 | 0.001 |
| 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".