A ten-year community reporting database reveals rising coyote boldness and associated human concern in Edmonton, Canada
Bibliographic record
Abstract
In cities throughout North America, sightings of coyotes (<em>Canis latrans</em>) have become common. Reports of human-coyote conflict are also rising, as is the public demand for proactive management to prevent negative human-coyote interactions. Effective and proactive management can be informed by the direct observations of community members, who can report their interactions with coyotes and describe the location, time, and context that led to their interactions. To better understand the circumstances that can predict human-coyote conflict, we used a web-based reporting system to collect 9134 community-supplied reports of coyotes in Edmonton, Canada, between January 2012 and December 2021. We used a standardized ordinal ranking system to score each report on two indicators of human-coyote conflict: coyote boldness, based on the reported coyote behavior, and human concern about coyotes, determined from the emotions or perceptions about coyotes expressed by reporters. We assigned greater scores to behaviors where coyotes followed, approached, charged, or contacted pets or people, and to perceptions where reporters expressed fear, worry, concern, discomfort or alarm. Using ordered logistic regression and chi-square tests, we compared boldness and concern scores to spatial, temporal, and contextual predictors. Our analysis showed that coyotes were bolder in less developed open areas and during the pup-rearing season, but human concern was higher in residential areas and during the dispersal season. Reports that mentioned dogs or cats were more likely to describe bolder coyote behavior, and those that mentioned pets or children had more negative perceptions about coyotes. Coyote boldness and human concern both indicated rising human-coyote conflict in Edmonton over the 10 years of reporting.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".