Bibliographic record
Abstract
Coyote (Canis latrans) presence in many North American cities evokes fear in some humans, driving demands for management action. With societal values shifting towards non-lethal coexistence practices, many wildlife managers turn to strategies like aversion conditioning, designed to increase coyotes' fear of humans. Yet, scant knowledge exists about baseline fear behaviors (e.g., vigilance, alertness) in urban coyotes. This has implications for coexistence practices, as the motivation for coyotes' behavior should underscore how managers respond. To explore urbanization effects on fear and behavior, we used remote cameras to monitor three coyote families during the pup-rearing season in urban, peri-urban, and rural sites in/near Calgary, Canada (2021-2022). We coded behaviors observed in adults and pups using 62 822 images. Rural adult coyotes were observed more around pups, while urban and peri-urban coyotes were observed more around pups that were playing, spent more time den-guarding, and showed higher alertness. This adaptive response in urban and peri-urban coyotes may force some coyotes into a behavioral trade-off (e.g., guarding pups vs. foraging), which could translate into more risky behaviors (e.g., consuming garbage). The elevated baseline fear in coyotes facing urban pressures suggests that coexistence practitioners must consider the risks of increasing fear during aversion conditioning.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".