Quantifying carnivory by grizzly bears in a multi‐ungulate system
Bibliographic record
Abstract
Abstract Grizzly bears (Ursus arctos) cohabit landscapes with a diversity of ungulates, but the degree to which grizzly bears are carnivorous is unclear and likely varies across landscapes. We used stable isotopes of carbon and nitrogen to infer seasonal diets of grizzly bears in northern British Columbia while studying predator–prey dynamics in a largely undisturbed system. We found high seasonal variation in diets among individuals; males consumed more ungulate prey than did females throughout the year. In fall, both sexes increased their consumption of prey; large ungulates constituted 51% and 32% of the fall diets of males and females, respectively. This increase in carnivory appeared to be primarily associated with consumption of elk (Cervus elaphus). Estimates of prey proportions in the diets of grizzly bears were highly sensitive to the range of tissue to diet discrimination values that were incorporated in isotope models. Small changes in discrimination values resulted in estimated prey intakes that varied more than threefold as a percentage of the diet depending on sex and season. We caution against using standard discrimination values, and we recommend that diet reconstructions using stable isotopes be based on tissue‐specific values that would be appropriate for the species of interest. © 2012 The Wildlife Society.
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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 | 0.000 |
| 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".