Environmental inversion using bowhead whale calls in the Chukchi Sea
Bibliographic record
Abstract
This paper estimates environmental properties of a shallow-water site in the Chukchi Sea using bowhead whale calls recorded on an asynchronous cluster of ocean-bottom hydrophones. Frequency-modulated whale calls with energy in at least two (dispersive) modes were recorded on a cluster of seven hydrophones within a 5 km radius. The frequency-dependent mode arrival times for nine whale calls were used as data in a Bayesian focalization inversion that considered the whale locations and range-independent environmental properties (sound-speed profile, water depth, and seabed geoacoustic profile) as unknown. A trans-dimensional inversion over the number of points defining the sound-speed profile and subbottom layers allows the data to determine the most appropriate environmental model parameterization. The whale-call instantaneous frequency, relative recorder clock drifts, and residual-error standard deviation are also unknown parameters in the inversion which provides uncertainty estimates for all model parameters and parameterizations. The sound-speed profile shows poor resolution but the thickness and sound speed for the upper sediment layer are reasonably well resolved. Results are compared to an inversion of controlled-source (airgun) dispersion data collected nearby which showed higher environmental resolution.
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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".