Quantifying a stopover of killer whales preying on gray whales rounding the Alaska Peninsula
Bibliographic record
Abstract
Predation by killer whales Orcinus orca on recovering populations of baleen whales is being increasingly reported, but there have been no direct quantitative assessments of its importance, for either the predators or prey. We used photographic mark-recapture and satellite telemetry to assess the abundance and behavior of killer whales gathering to feed on gray whale Eschrichtius robustus calves and juveniles that were migrating around the Alaska Peninsula into the Bering Sea. We quantitatively describe this aggregation as a stopover, used in May and early June 2003-2008 by at least 197 different killer whales. Estimates of average stopover duration (1.8-3.6 wk) and annual abundance (89-128 killer whales) were variable and correlated with annual gray whale calf production (404-1528 calves). In years with more gray whale calves, killer whales spent more time at this geographic pinch point where they could presumably access adequate prey. In years with fewer calves, more killer whales used the study area but remained for a shorter time, presumably searching more widely when calves were scarcer. The presence of killer whales increased through May into early June, when satellite tags tracked northerly movements into the Bering Sea, and as far as 1620 km into the Chukchi Sea, likely following migrating gray whales. These data indicate focused and prolonged predation by killer whales on a recovered population of baleen whales and provide the first evidence of the importance of such predation in structuring killer whale populations and influencing their dynamics.
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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 | 0.001 | 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".