The diving behaviour of mammal-eating killer whales (<i>Orcinus orca</i>): variations with ecological not physiological factors
Bibliographic record
Abstract
Mammal-eating killer whales ( Orcinus orca (L., 1758)) are a rare example of social predators that hunt together in groups of sexually dimorphic adults and juveniles with diverse physiological diving capacities. Day–night ecological differences should also affect diving as their prey show diel variation in activity and mammal-eating killer whales do not rely on echolocation for prey detection. Our objective was to explore the extent to which physiological aerobic capacities versus ecological factors shape the diving behaviour of this breath-hold diver. We used suction-cup-attached depth recorders (Dtags) to record 7608 dives of 11 animals in southeast Alaska. Analysis of dive sequences revealed a strong bout structure in both dive depth and duration. Day–night comparisons revealed reduced rates of deep dives, longer shallow dives, and shallower long-duration dives at night. In contrast, dive variables did not differ by age–sex class. Estimates of the aerobic dive limit (cADL) suggest that juveniles exceeded their cADL during as much as 15% of long dives, whereas adult males and females never exceeded their cADL. Mammal-eating killer whales in this area appear to employ a strategy of physiological compromise, with smaller group members diving nearer their physiological limits and large-bodied males scaling down their physiological performance.
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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 | 0.000 |
| 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".