Eastern Coyotes (<i>Canis latrans</i> var.) consuming large ungulates in a multi-ungulate system
Bibliographic record
Abstract
The feeding habits of generalist predators often vary among populations and regions. For example, Coyote (Canis latrans), which is a generalist predator distributed across North America, occupies a wide range of habitats and has a highly varied diet. In this observational study, we quantified the presence of mammalian prey items in 50 Eastern Coyote (Canis latrans var.) scats collected in late spring and summer in a private game reserve in southwestern Quebec. Nearly all scats contained hair of White-tailed Deer (Odocoileus virginianus; 44%), Moose (Alces americanus; 38%), or American Beaver (Castor canadensis; 38%). Although all three species are known to be consumed by coyotes, such a high proportion of Moose and White-tailed Deer simultaneously occurring in the diet of coyotes has not been previously reported. The uniqueness of the study area, with its relatively high abundance of all three prey species, may account for the uniqueness of the diet of Eastern Coyotes living there.
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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 | 0.001 |
| 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) | 0.002 | 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".