Coyote Attacks on Humans, 1970-2015
Bibliographic record
Abstract
Beginning with the developing pattern of urban and suburban coyotes attacking humans in southern California in the late 1970s, we have gathered information on such incidents in an effort to better understand the causes of such changes in coyote behavior, as well as to develop strategies that can reduce the incidence of such attacks. Here, we update information from our knowledge of conflicts between humans and coyotes occurring largely in urban and suburban environments in the United States and Canada during the past 30 years. This problem emerged in states beyond California and in Canadian provinces in the 1990s, and it appears to be growing. We have documented 367 attacks on humans by coyotes from 1977 through 2015, of which 165 occurred in California. Of 348 total victims of coyote attack, 209 (60%) were adults, and 139 (40%) were children (age ≤10 years). Children (especially toddlers) are at greater risk of serious injury. Attacks demonstrate a seasonal pattern, with more occurring during the coyote breeding and pup-rearing season (March through August) than September through February. We reiterate management recommendations that, when enacted, have been demonstrated to effectively reduce risk of coyote attack in urban and suburban environments, and we note limitations of non-injurious hazing programs. We note an apparent growing incidence of coyote attack on pets, an issue that we believe will drive coyote management policy at the local and state levels.
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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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".