Foods and nutritional components of diets of black bear in Rocky Mountain National Park, Colorado
Bibliographic record
Abstract
We used scat analysis to determine diets and relative nutritional values of diets for black bears ( Ursus americanus Pallas, 1780) in Rocky Mountain National Park, Colorado, from 2003 to 2006, and compared foods consumed and nutritional components to identify important sources of fecal gross energy (GE), crude fat (CF), and fecal nitrogen (FN) in annual and seasonal diets. Patterns of use of food classes followed typical seasonal patterns for bears, although use of animal matter was among the highest reported (>49% annually). Use of animal matter increased after spring, although crude protein levels in bear diets were always >25%. GE was typically lowest for grasses and other herbaceous plants and highest for ants and ungulates; FN was strongly positively related to most animal sources, but negatively correlated with vegetative matter; and CF showed the strongest positive relationship with ungulates and berries, with the latter likely influenced by the presence of seeds. Compared with historic data (1984–1991), contemporary diets included substantially greater prevalence of anthropogenic foods, which likely contributed to increases in size, condition, and productivity of the contemporary bear population. Management strategies are needed to increase quantity and quality of natural foods while minimizing dependence on anthropogenic sources.
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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) | 0.000 | 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".