Bibliographic record
Abstract
We summarize previously published information on coyote attacks on humans in North America. This problem has developed primarily in urban and suburban areas of southern California since the early 1970s, and the frequency of attacks and other human safety incidents is increasing. Similar attacks are now known from at least 18 states in addition to California and from 4 Canadian provinces, with the majority of attacks occurring since the early 1990s. We review early explorers' and settlers' accounts of coyotes in the Los Angeles area, as well as development of coyote control programs during the 20th century. We also describe the political and human dimensions aspects of attempts to manage suburban coyotes, noting that a wide range of beliefs and opinions can be present among city-dwellers. We believe the most important factors contributing to coyotes' habituation to humans, which in southern California has led to coyote aggression and attacks, are: residential habitats rich in resources; reduced efforts to control coyote populations; and changing human attitudes and behavior toward coyotes. Similar circumstances in other suburban habitats in North America may have led to increased coyote attacks elsewhere, but it is difficult to predict if they will become as numerous as in southern California.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".