Three-dimensional Bayesian passive acoustic tracking of walruses in the Chukchi Sea.
Bibliographic record
Abstract
A Bayesian travel-time inversion method was developed for 3-D localization and tracking using multipath arrival times of knock-type sounds produced by walruses. The method accounts for data errors and uncertainties in environmental and geometric parameters. Data were collected on three seafloor sound recorders, arranged in an equilateral triangle with approximately 500-m sides, deployed in 30 m of water in the Chukchi Sea near the Hanna Shoal, west of Barrow, Alaska. Pacific Walrus (Odobenus rosmarus divergens) calls were recorded by these recorders for 2.5 months in the summer of 2009. A regularized, linearized inversion algorithm was used to estimate 3-D tracks and track uncertainties for these calling walruses. Regularization incorporates prior information (expected values and uncertainty estimates) for the recorder locations and acoustic environment (water depth and sound speed) as well as preferred track characteristics. Inverting for the smoothest (simplest) track consistent with the acoustic data and prior information mitigates the risk of over-interpreting track structure due to data errors and environmental and geometric uncertainties. Hence, this approach provides more plausible sequences of source positions than a non-regularized solution. Tracks for walrus dives from the 2009 dataset will be presented.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".