Urban coyotes were observed rarely and retreated consistently from assertive approaches by volunteers in neighborhoods
Bibliographic record
Abstract
Human-coyote conflict can arise when coyotes follow, pursue or attack pets or people. Although such attacks are rare, they are typically highly publicized, and leave residents concerned about the presence of coyotes in their neighborhoods. Wildlife managers often promote hazing to mitigate human-coyote conflicts, but this technique for intimidating animals has been studied in coyotes only recently and there are few guidelines for its implementation. We developed a community-based hazing program in Edmonton (Alberta, Canada) implemented by volunteers who patrolled their neighborhoods while recording coyotes and coyote attractants. When coyotes were observed, volunteers walked towards the coyotes and recorded their overt reaction and flight initiation distances. If coyotes did not retreat when volunteers were within 40 m of the animal, volunteers ran towards the coyote while shouting and throwing weighted tennis balls towards the animals. Over two field seasons, we recruited, trained, and engaged 120 volunteers from 71 neighborhoods, who conducted 1598 patrols, observed coyotes in 175 instances, and conducted hazing 23 times. Coyotes retreated before volunteers were within 40 m during 124 (71%) of the observations and retreated immediately from 22 (96%) of the hazing events. We found no evidence that hazing changed subsequent measures of overt reaction or flight initiation distances by coyotes, perhaps because it was implemented too rarely, and its effects on the number or timing of subsequent coyote reports by members of the public were inconsistent. Our study emphasizes the rarity of close encounters with coyotes and the high frequency with which they retreat from human approaches and even directed attention by people. Our study supports the use of community-based hazing to reassure members of the public while potentially promoting wariness in coyotes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".