Behavioral context of echolocation and prey‐handling sounds produced by killer whales (<i>Orcinus orca</i>) during pursuit and capture of Pacific salmon (<i>Oncorhynchus</i> spp.)
Bibliographic record
Abstract
) in the northeastern Pacific. Although piscivorous killer whales rely on echolocation to locate and track prey, the relationship between echolocation, movement, and prey capture during foraging by wild individuals is poorly understood. We used acoustic biologging tags to relate echolocation behavior to prey pursuit and capture during successful feeding dives by fish-eating killer whales in coastal British Columbia, Canada. The significantly higher incidence and rate of echolocation prior to fish captures compared to afterward confirms its importance in prey detection and tracking. Extremely rapid click sequences (buzzes) were produced before or concurrent with captures of salmon at depths typically exceeding 50 m, and were likely used by killer whales for close-range prey targeting, as in other odontocetes. Distinctive crunching and tearing sounds indicative of prey-handling behavior occurred at relatively shallow depths following fish captures, matching concurrent observations that whales surfaced with fish prior to consumption and often shared prey. Buzzes and prey-handling sounds are potentially useful acoustic signals for estimating foraging efficiency and determining if resident killer whales are meeting their energetic requirements.
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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 | 0.000 | 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) | 0.001 | 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".