Implications of a high-energy and low-protein diet on the body composition, fitness, and competitive abilities of black (<i>Ursus americanus</i>) and grizzly (<i>Ursus arctos</i>) bears
Bibliographic record
Abstract
Plants are not ideal foods for bears yet many populations are largely vegetarian. Implications of this diet on the body composition, fitness, and competiveness of black ( Ursus americanus Pallas, 1780) and grizzly ( Ursus arctos L., 1758) bears have had limited field investigation. The analysis of scats of grizzly and black bears from the Flathead valley, British Columbia, suggest seasonal dietary differences between species, but >85% of the summer diet of both species were fruits that are low in protein. Body composition measurements showed bears loose fat during spring, gained fat during summer, and grizzly bears were leaner than black bears. Individual black bears gained mass up to 2.7 times faster than theory predicted. Bears rapidly gained fat but lost lean tissues while feeding on fruit, suggesting that lean tissues were used to buffer seasonal protein shortages. Comparisons among populations of grizzly bears without access to salmon revealed the amount of meat in the diet was positively related with adult female mass but negatively related with bear density. Bears have the behavioural and phenotypic plasticity which enables populations that focus their foraging on plants to have small but fat females and live at higher densities than populations that focus more on obtaining terrestrial meat.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".