Selective predation by brown bears (<i>Ursus arctos</i>) foraging on spawning sockeye salmon (<i>Oncorhynchus nerka</i>)
Bibliographic record
Abstract
Selective predation by and predation rates of brown bears (Ursus arctos) foraging on spawning sockeye salmon (Oncorhynchus nerka) in a small shallow creek in the Wood River lake system near Bristol Bay, Alaska, were quantified during 1986 and 19901992. Bears killed a high proportion of spawning salmon when few salmon entered the creek (92% of 505 fish) and a much smaller proportion when the spawning population reached a historical high (16% of 15 631 fish). Selective predation on salmon that differed in length, sex, and spawning condition was measured by tagging salmon at the mouth of the creek immediately prior to upstream migration and then recovering dead tagged fish during daily surveys of the entire creek. The relative frequencies of large, medium-sized, and small salmon killed by bears indicated that the risk of predation was more than 150% greater for large than for small salmon. A higher proportion of the male salmon population was killed and a greater proportion of male bodies were consumed than female salmon. Selectivity for male salmon increased as the spawning season progressed, possibly because male salmon weakened earlier and lived longer in a weakened state than female salmon. Male salmon were attacked mostly along the dorsal hump area, whereas female salmon tended to be attacked along the abdomen, where eggs could be exposed. Bears selectively killed female salmon prior to spawning during 1 of the 3 years, but only 6.17.8% of the female spawning populations were killed prior to spawning. These data support the hypothesis that selective predation by bears may influence the body morphology of spawning salmon.
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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".