Data from: Poor health is associated with use of anthropogenic resources in an urban carnivore
Bibliographic record
Abstract
Rates of encounters between humans and wildlife are increasing in cities around the world, especially when wildlife overlap with people in time, space and resources. Coyotes (Canis latrans) can make use of anthropogenic resources and reported rates of conflict have increased in cities across North America. This increase may be linked to individual differences in the use of human food and developed areas. We compared the relationships between coyote age, sex or health and the use of anthropogenic resources, which we defined as using developed areas over large home ranges, being active during the day, and consuming anthropogenic food. To do so, we applied GPS collars to 19 coyotes and sampled hair for stable isotope analysis. Eleven coyotes appeared to be healthy and eight were visibly infested with sarcoptic mange (Sarcoptes scabiei), a mite that causes hair loss. Diseased coyotes used more developed areas, had larger monthly home ranges, were more active during the day, and assimilated less protein than coyotes that appeared to be healthy. We speculate that anthropogenic food provides a low-quality but easily accessible food source for diseased coyotes, which in turn may increase reliance on it and other anthropogenic resources to promote encounters with people.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.034 | 0.016 |
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".