Major components of grizzly bear diet across North America
Bibliographic record
Abstract
We measured stable carbon and nitrogen isotope ratios in guard hair of 81 populations of grizzly bears (Ursus arctos L., 1758) across North America and used mixing models to assign diet fractions of salmon, meat derived from terrestrial sources, kokanee (Oncorhynchus nerka (Walbaum in Artedi, 1792)), and plants. In addition, we examined the relationship between skull size and diet of bears killed by people in British Columbia. The majority of carbon and nitrogen assimilated by most coastal grizzly bear populations was derived from salmon, while interior populations usually derived a much smaller fraction of their nutrients from salmon, even in areas with relatively large salmon runs. Terrestrial prey was a large part of the diet where ungulates were abundant, with the highest fractions observed in the central Arctic, where caribou (Rangifer tarandus (L., 1758)) were very abundant. Bears in some boreal areas, where moose (Alces alces (L., 1758)) were abundant, also ate a lot of meat. Bears in dryer areas with low snowfall tended to have relatively high meat diet fractions, presumably because ungulates are more abundant in such environments. Kokanee were an important food in central British Columbia. In areas where meat was more than about a third of the diet, males and females had similar meat diet fractions, but where meat was a smaller portion of the diet, males usually had higher meat diet fractions than females. Females reached 95% of their average adult skull length by 5 years of age, while males took 8 years. Skull width of male grizzly bears increased throughout life, while this trend was slight in females. Skull size increased with the amount of salmon in the diet, but the influence of terrestrial meat on size was inconclusive. We suggest that the amount of salmon in the diet is functionally related to fitness in grizzly 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".