Knowledge increases informative reporting by the public about urban coyotes
Bibliographic record
Abstract
Many municipalities use information about human-wildlife interactions collected in citizen-provided reports to monitor conflicts and guide management actions. However, high volumes of reports that describe benign wildlife behaviour can reduce the efficiency with which officials address reports that require management interventions, a situation that has occurred in Edmonton, Canada. We used data from a survey of Edmonton residents (n = 5,926) that asked respondents to anticipate whether they would alert officials if they witnessed (a) a coyote exhibiting benign behaviour in a natural area and (b) conflict-prone behaviour near human dwellings. We used path models to explore the predictors for two response variables; the agreement with reporting a benign sighting and the difference between scores for agreement to report conflict-prone behaviour and a benign sighting, which we interpreted as more informed reporting. As predictor variables, we considered a set of demographic, situational, cognitive, and experiential factors measured in the survey. A greater tendency to report the benign sighting was associated with prior 311 calls, higher risk perceptions, having experienced less severe interactions with coyotes, and greater knowledge of the consequences of food conditioning in coyotes. A greater tendency to anticipate reporting a conflict-prone coyote was associated with lower risk perceptions and greater knowledge of the consequences of food conditioning in coyotes, which is frequently associated with conflict. Further, individuals with higher risk perceptions were more likely to have experienced more severe interactions with coyotes, which were sometimes associated with living on a greenspace. Our results suggest that education campaigns could help people recognize benign behavior and identify and mitigate potential conflicts with coyotes. Education could occur as part of report receipt by city staff and outreach could target areas where coyote interactions are more likely, such as in residential areas along greenspaces.
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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 | 0.002 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".