Individual flexibility in nocturnal activity reduces risk of road mortality for an urban carnivore
Bibliographic record
Abstract
Many species living in developed areas adjust the timing of their activity and habitat selection to avoid humans, which may reduce their risk of conflict, including vehicle collisions. Understanding the behavioral adaptations to vehicles exhibited by species that thrive in urban areas could improve the conservation of many species that are threatened by road-caused mortality. We explored these behaviors using the seasonal distribution of 80 road-killed coyotes ( Canis latrans ) collected by civic employees and by comparing the activity patterns (step lengths) and road crossings made by 19 coyotes fitted with GPS collars with 3-h fix rates, 7 of which were killed in vehicle collisions. Coyotes were collected on roads most often in spring and fall, which corresponded to the most rapid changes in day length in our northern study area and when collared road-killed coyotes were more active during rush hour. Among collared coyotes, those that were killed on roads were most active and crossed roads most frequently at dusk. By contrast, surviving animals were most active and crossed roads most often near midnight year round and surprisingly, exhibited less avoidance of roads than did road-killed coyotes. Our results suggest that risk of vehicle collision is lower for coyotes that restrict the times at which they cross roads but some coyotes do not or cannot. Such behavioral flexibility to adapt to the timing of human activity relative to exogenous cues such as dawn and dusk may contribute to differences both among and within wildlife species in rates of coexistence with humans.
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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 | 0.000 | 0.000 |
| 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".