Habitat features mediate selective consumption of salmon by bears
Bibliographic record
Abstract
Salmon provide a key source of marine-derived nutrients to aquatic and surrounding terrestrial habitats in coastal areas of the North Pacific. Bears are a major predator of salmon and provide an important pathway for carcass transfer to riparian zones. We studied selective consumption of salmon (Oncorhynchus keta and Oncorhynchus gorbuscha) by bears (Ursus arctos and Ursus americanus) on 12 streams on the central coast of British Columbia, Canada. We predicted that bears would select more energy-rich parts, and eat less of each fish (i.e., selective consumption), in streams with more prey and simpler habitat (i.e., streams that facilitate salmon capture). Bears were 12% more likely to consume fish selectively in narrow, shallow streams with less pool volume, where salmon are easier to catch, than in deep, wide streams. However, bears were also 21% more likely to selectively consume fish in streams with more wood obstacles and undercut banks, where hunting was predicted to be more difficult. This suggests that stream characteristics can have significant indirect effects on riparian nutrient subsidies to ecosystems through selective feeding by bears.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".