The relative importance of prey density and social dominance in determining energy intake by bears feeding on Pacific salmon
Bibliographic record
Abstract
We quantified foraging behavior of brown bears (Ursus arctos) feeding on adult chum (Oncorhynchus keta) and pink salmon (Oncorhynchus gorbuscha) at three small coastal streams in southeastern Alaska from streamside tree stands. These observations revealed that social dominance was much more important in determining intake rates among bears than salmon densities. Each small stream supported one large, socially dominant bear that directly displaced other bears in aggressive encounters or was avoided in "passive deferrals". Although the number of fish killed per foraging bout was positively correlated with salmon density, energy intake was determined primarily by foraging effort, as dominant bears visited the stream more often and foraged for longer periods than subdominant bears. Capture efficiency (fish captured per minute searching) was highly variable and increased only marginally with salmon density and among social ranks. Subdominant bears were more vigilant, used a smaller fraction of each stream, and carried salmon much farther into the forest prior to consumption, presumably to minimize interactions with other bears. Social dominance may play an important role in regulating reproductive success when salmon densities are low and may have important implications for managers in bear-viewing areas.
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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 | 0.000 | 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".