Temporal variation in food habits of the American Black Bear (<em>Ursus americanus</em>) in the boreal forest of northern Ontario
Bibliographic record
Abstract
We used scat analysis to investigate temporal variation in the food habits of American Black Bears (Ursus americanus) in the boreal forest of northern Ontario. Specifically, we examined whether there was a seasonal shift in foraging over three years (1990–1992) and which foods, if any, varied in occurrence among years. American Black Bears ate foods ranging from green vegetation in the spring to ants in mid-summer and berries and nuts in late summer and fall. Late summer berry forage, especially blueberries (Vaccinium spp.), varied greatly among years. American Black Bears in northern Ontario consumed a variety of foods opportunistically. Understanding how American Black Bears in northern Ontario exploit food resources and how these food items vary among years can provide insights into demographic processes and help wildlife managers better anticipate changes in the structure of the harvest of American Black Bears and in human–bear conflict levels. In northern Ontario, forest management practices that increase the availability of early successional species would be beneficial to American Black Bear populations.
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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".