Coyote (<i>Canis latrans</i>) diet and spatial co-occurrence with woodland caribou (<i>Rangifer tarandus caribou</i>)
Bibliographic record
Abstract
The interplay of predator encounters and antipredator responses is an integral part of understanding predator–prey interactions and spatial co-occurrence and avoidance can elucidate these interactions. We conducted hard-part dietary analysis of coyotes (Canis latrans Say, 1823) and space use of coyotes and woodland caribou (Rangifer tarandus caribou (Gmelin, 1788)) to test two competing hypotheses about coyote and caribou predator–prey spatial dynamics using resource selection functions. The high encounter hypothesis predicts that coyotes would maximize encounters with caribou via high spatial co-occurrence, whereas the predator stealth hypothesis predicts that through low spatial co-occurrence with caribou, coyotes act as stealth predators by avoiding habitats that caribou typically select. Our dietary analysis revealed that ∼46% of sampled coyote diet is composed of caribou. We found that coyote share space with caribou in lichen-barren habitat in both summer and winter and that coyotes co-occur with caribou in forested habitat during summer, but not during winter. Our findings support predictions associated with the high encounter predator hypothesis whereby coyotes and caribou have high spatial co-occurrence promoting caribou in coyote diet.
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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 | 0.000 | 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".