Predictable features attract urban coyotes to residential yards
Bibliographic record
Abstract
ABSTRACT Several species of urban‐adapted carnivores, including coyotes ( Canis latrans ), use anthropogenic resources in residential areas, which may increase rates of encounters and conflict with people. These negative interactions might be reduced with more understanding of individual variation in the use of residential areas and if attractants were better predicted by residents and targeted for securement or removal. We fitted 19 urban coyotes with global positioning system (GPS) collars (11 healthy, 8 with sarcoptic mange [ Sarcoptes scabiei ]) and compared their selection for residential areas at different times of day. We also identified 173 clusters of GPS locations (representing foraging and bedding sites) and paired them with available sites to measure selection for anthropogenic food, shelter, and visual cover. Seventeen of 19 coyotes avoided residential areas in general, but lesser avoidance of residential areas occurred in animals that used these areas during the day. Backyards selected by coyotes were 66.7 times less likely to have fences, 22.2 times more likely to contain anthropogenic food, and had 3.3 times as much visual cover than available yards. Diseased coyotes were 9 times more likely than healthy animals to select for yards with anthropogenic food. Our results suggest that coarse measurements of habitat selection via land cover classes may underestimate the attraction to wildlife of particular features in residential areas. Greater management of these features by municipal governments, residents, and communities might reduce animal use, disease transmission, and human‐wildlife conflict for diverse species in urban areas. © 2017 The Wildlife Society.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".