Factors influencing the seasonal diet selection by woodland caribou (<i>Rangifer tarandus tarandus</i>) in boreal forests in Ontario
Bibliographic record
Abstract
We used remote video cameras to assess seasonal diet composition of woodland caribou (Rangifer tarandus tarandus (L., 1758)) at three areas across the boreal forest of Ontario. Caribou consume lichens in winter, but we expected they would significantly reduce lichen consumption in favour of higher protein levels in green plants in summer. We sampled videos from 23 caribou, from 2 years, to derive seasonal diet composition. Diet differed among seasons and study areas, except in winter when lichens dominated. Diet breadth doubled from winter to summer, but overlap between seasons was still >60%. Green plants were less commonly eaten than we expected, only three genera were preferred, and few species constituted more than 2% of the diet. Preferred foods varied by land-cover types. Diet differed between managed and unmanaged landscapes but did not result from use of plant species found in successional habitats. Caribou selected a nonoptimal diet in the snow-free seasons, especially with respect to protein, suggesting factors other than nutrition influenced diet choice, and indicating the possibility of bottom-up limitation on production.
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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 | 0.000 |
| 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".