Acoustic localization of resident killer whales for source-level estimation of echolocation clicks
Bibliographic record
Abstract
To study the efficacy of resident killer whale echolocation clicks for foraging and other activities requires knowledge of the acoustic characteristics of their clicks, in particular, source levels. To compute apparent source level (ASL) from recordings of killer whales in the wild requires localization of the clicking animals, ideally including rigorous uncertainty estimates, to account for transmission losses from source to receiver. This paper considers acoustic localization of killer whales based on time-of-arrival-differences for clicks recorded at a 2 × 2 m2 array of 23 hydrophones. To quantify uncertainties, a Bayesian localization approach is formulated and two methods of solution are considered, one based on a linearized approximation and the other a nonlinear three-dimensional (3D) grid search. Simulations indicate significant linearization errors in 3D uncertainty (probability) distributions, confirming the superiority of the nonlinear localization. Results of this localization approach are used to calculate ASLs (with uncertainties) for echolocation clicks from Southern and Northern Resident killer whales determined to be directed approximately at the array (i.e., on-axis). Comparison of ASL values across varying ranges indicates a roughly logarithmic range dependence, consistent with whales adjusting their click levels based on distance to the target, approximately accounting for two-way spherical-spreading loss.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".