Using accelerometry tags to quantify gray whale foraging behavior
Bibliographic record
Abstract
Abstract High‐resolution tri‐axial accelerometry biologging tags have quantitatively described behaviors in baleen whale species that forage using lunges and continuous ram filtration. However, detailed quantitative descriptions of foraging behaviors do not exist for gray whales, a unique baleen whale species that primarily uses benthic suction feeding with a rolling component. We deployed suction cup biologging tags on Pacific Coast Feeding Group (PCFG) gray whales to quantify foraging behavior at the broad state (dive) and foraging tactic (roll event) scales. Hidden Markov models were used to describe three distinct states using turn angle, dive duration, pseudotrack tortuosity, and presence of roll events that can be interpreted as forage, search, and transit behavior. Classification and Regression Tree models best described foraging tactics (headstands, benthic digs, and side swims) using median pitch, depth to total length ratio, and absolute value of the median roll. On average, PCFG gray whales spent more time searching and performed more left‐rolled foraging tactics at shallower depths at night compared to during the day, potentially to track prey above them in the water column. Describing foraging behavior in PCFG gray whales enables examination of links between behavioral budgets, energetics, and the physiological impact of threats facing this group.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".