Acoustic behavior of gray whales tagged with biologging devices on foraging grounds
Bibliographic record
Abstract
Understanding the function of baleen whale acoustic signals requires the investigation of calling behaviors relative to location, timing, and behavioral state. Previous studies of gray whale ( Eschrichtius robustus ) vocal behavior have principally used passive recorders and been conducted primarily on breeding grounds and along migratory routes, and not areas associated with foraging. We conducted an analysis of accelerometer and acoustic data collected during 12 deployments of animal-borne tags in 2016, 2019, and 2021 on gray whales that return annually to northern Puget Sound, Washington. We identified 141 calls from gray whales in approximately 128 hours of accelerometer and/or acoustic data collected from tag deployments. The most prominent were pulsive ‘rumble-like’ calls, upsweeping tones, and moans. Using the tag’s accelerometer to identify calls produced by tagged animals enabled us to explore the behavioral context behind call production, revealing that vocalizations were made primarily at slow speeds (mean 1.03 ± 0.26 m/s -1 ), shallow depths (mean 7.63 ± 4.99 m), and temporally proximate to surface behavior (mean 43.3 ± 39.66 sec) most similar to non-foraging associated depth (mean 5.78 ± 1.46 m and speed (mean 1.46 ± 1.11 m/s -1 ) profiles. Vocalizations originating from the tagged animal occurred closer to the conclusion of a foraging event (mean 87.7 ± 119.32 min) than the beginning, with only 7% of all calls occurring during periods of feeding. This study demonstrates that gray whales remain vocal on foraging grounds and that call-associated accelerometry signals can be a valuable tool in identifying individual callers in animal-borne acoustic data.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".