Coyote (<i>Canis latrans</i>) diet in an urban environment: variation relative to pet conflicts, housing density, and season
Bibliographic record
Abstract
Coyotes (Canis latrans Say, 1823) are highly successful in urbanized environments, but as they populate cities, conflict can occur and often manifests in the form of incidents with pets. To better understand whether coyotes view pets as prey or, alternatively, as competitors or a threat, we conducted a diet analysis of coyotes in the Denver metropolitan area (DMA) by analyzing scats. We also examined differences in diet between high- and low-density housing and among seasons. We found only small percentages of trash and domestic pets in the coyote diet. The presence of pets in the diet did not coincide with the increase of pet conflicts in the DMA in December and January, supporting the hypothesis that coyote conflict with pets is primarily driven by competition or a threat response. Coyotes relied mostly on native plant and animal species, and rodents and lagomorphs were the most prevalent diet items. Coyotes consumed rodents and non-native plants more often in high-density housing and deer, corn, and native plants more often in low-density housing. Coyotes also consumed more fruits and invertebrates during summer and autumn and more mammals and birds in winter and spring. As human–coyote conflicts increase in urban areas, understanding how coyotes and other urban-adapted carnivores use anthropogenic resources may provide insight that can be used to promote coexistence between humans and wildlife.
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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 | 0.000 | 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".