Patterns of wild carnivore attacks on humans in urban areas
Bibliographic record
Abstract
Attacks by wild carnivores on humans represent an increasing problem in urban areas across North America and their frequency is expected to rise following urban expansion towards carnivore habitats. Here, we analyzed records of carnivore attacks on humans in urban areas of the U.S. and Canada between 1980 and 2016 to analyze the general patterns of the attacks, as well as describe the landscape structure and, for those attacks occurring at night, the light conditions at the site of the attacks. We found that several behavioral and landscape-related factors were recurrent elements in the attacks recorded. The species for which the attack locations were available (coyote and black bear) attacked in areas with different conditions of landscape structure and artificial light. Specifically, black bears attacked more frequently in areas with abundant and aggregated vegetation cover and scarce buildings and roads, while coyotes attacked in a broader range of landscape conditions. At night, black bears attacked in generally darker areas than coyotes. By providing a comprehensive perspective of the phenomenon, this study will improve our understanding of how effective strategies aimed at reducing the frequency of risky encounters in urban areas should be developed.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".