A Case History of Wolf-Human Encounters in Alaska and Canada
Bibliographic record
Abstract
Executive Summary Currently there are an estimated 59,000-70,000 wolves (Canis lupus) in Alaska and Canada. Past reviews of wolf-human interactions concluded that wild, healthy wolves in North America present little threat to human safety. However, since 1970 some cases have appeared in the published literature documenting wold aggression toward people. A wolf attack on a 6-year-old boy near Icy Bay, Alaska in April 2000 generated debate in Alaska that challenged previous assumptions regarding the potential danger of wolves to people. At that time there was no recently compiled record of wolf-human encounters for either Alaska or Canada. To provide a current perspective on wolf-human interactions, I compiled a case history that describes 80 wolf-human encounters in which wolves showed little fear of people. I obtained cases from biologists and law enforcement officers in Alaska and Canada, from public health records, from the published literature, and from interviews with private citizens who witnessed the events. I classified the 80 cases into 7 behavioral categories: 1) Agonism, 2) Predation, 3) Prey Testing or Agnostic Charges, 4) Self-Defense, 5) Rabies, 6) Investigative Searches, and 7) Investigative Approaches. Patterns of wold behavior described in this case history provide a reference for management of wolves where frequent wolf-human encounters occur. Thirty-nine cases contain elements of aggression among healthy wolves, 12 cases involve known or suspected rabid wolves, and 29 cases document fearless behavior among non-aggressive wolves. In 6 cases in which healthy wolves acted aggressively, the people were accompanied by dogs. Aggressive, non rabid wolves bit people in 16 cases; none of those bites was life-threatening, but in 6 cases the bites were severe.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.011 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".