FORAGING STRATEGIES OF SYMPATRIC KILLER WHALE (<i>ORCINUS ORCA</i>) POPULATIONS IN PRINCE WILLIAM SOUND, ALASKA
Bibliographic record
Abstract
Abstract Killer whales (Orcinus orca) feed on a wide variety of fish, cephalopods, and marine mammals throughout their cosmopolitan range; however, the dietary breadth that characterizes the species is not reflected in all populations. Here, we present the findings of a 14‐yr study of the diet and feeding habits of killer whales in Prince William Sound, Alaska. Two non‐associating forms of killer whale, termed resident and transient (Bigg et al. 1987), were identified. All prey seen taken by transients were marine mammals, including harbor seals (Phoca vitulina), Dall's porpoises (Phocoenoides dalli), Steller sea lions (Eumetopias jubatus), and harbor porpoises (Phocoena phocoena). Resident killer whales appeared to prey principally on salmon (Oncorhynchus spp.), preferring coho salmon (O. kisutch) over other, more abundant salmon species. Pacific herring (Clupea pallasi) and Pacific halibut (Hippocampus stenolepis) were also taken. Resident killer whales frequently were seen to interact in non‐predatory ways with Steller sea lions and Dall's porpoises, while transients were not. Differences in the social organization and behavior of the resident and transient killer whales in Prince William Sound are discussed in the light of the dietary differences documented here.
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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 | 0.001 |
| 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".