Some ecological and evolutionary aspects of bear-salmon interactions in coastal British Columbia
Bibliographic record
Abstract
I examine here quantity and characteristics of chum salmon (Oncorhynchus keta) captured by black bears (Ursus americanus) during autumn spawning migration in an old-growth watershed on Moresby Island (Haida Gwaii), western Canada. Spawning-salmon numbers ranged from 2300 to 6300 over 3 years of investigation (1992-1994) and there were a maximum of eight bears in the watershed. Following capture of a salmon, bears ate an average of 1.6 kg from each salmon carcass, including the brain, ovaries, and dorsal musculature, and generally tended to abandon viscera, testes, and bony remnants such as jaws. Complete counts of these jaws throughout the watershed in autumn 1993 demonstrated a total capture of 4281 salmon, for an average consumption rate of 13 salmon per day per bear over the 45-day spawning period. This comprised 74% of the salmon entering the stream (range among years 58-92%). Most salmon (70-80%) taken by bears were partially or completely spawned-out at the time of capture. Marginally but significantly higher predation rates occurred on males relative to their proportion in the stream, and on larger rather than smaller salmon of both sexes. Higher-quality salmon (larger, fresher) were transferred farthest from the capture site by bears, possibly to minimize competitive interference. Bear predation in this locality does not appear to seriously constrain total reproduction of the salmon, but it may have several genetic influences: (i) there may be selection against large body size of salmon in both males and females and (ii) high predation levels on partially spawned males may facilitate multiple paternity in spawning females and, therefore, increase effective genetic variance among fertilized eggs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.011 | 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 teacher head, 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".