The influence of human disturbance on Pacific salmon (<i>Oncorhynchus</i> spp.) in the diet of American black bears (<i>Ursus americanus</i>) in two areas of coastal British Columbia, Canada
Bibliographic record
Abstract
Recent studies have highlighted the importance of salmon (genus Oncorhynchus Suckley, 1861) in the diet of bears, and of bears as consumers and key agents supporting the transport of salmon-derived nutrients to riparian ecosystems. Salmon abundance and human disturbance are known influences on bear ecology and behaviour, though few studies have quantified shifts in bear diet due to these effects. We used stable isotope analysis to investigate how salmon escapement and human presence influenced the proportion of salmon in the diet of American black bears ( Ursus americanus Pallas, 1780) in two locations in coastal British Columbia, Canada. We found that salmon constituted a small proportion of black bear diet across sexes and ecosystems, while bears appeared to gain a similar amount of energy and lean mass from terrestrial sources. Salmon consumption was not related to the total annual abundance of salmon in a watershed but was significantly lower in large streams with regular human presence, suggesting that human disturbance can cause a dietary shift in bears that could have important consequences to their fitness. We also observed that the isotopic signatures of key bear foods did not vary between foliage and fruit, simplifying data collection for future isotopic studies on bear diet.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".