Fatal attacks by American black bear on people: 1900–2009
Bibliographic record
Abstract
Abstract At least 63 people were killed in 59 incidents by non‐captive black bear (Ursus americanus) during 1900–2009. Fatal black bear attacks occurred in Canada and Alaska (n = 49) and in the lower 48 states (n = 14). There were 3.5 times as many fatal attacks in Canada and Alaska but only 1.75 times as many black bears, and much less human contact for black bears in Canada and Alaska. There was a weak positive correlation (rs = 0.56, P ≤ 0.000) between the estimated size of a bear population within a given jurisdiction and the number of fatal black bear attacks. Some jurisdictions had no fatal black bear attacks but had large estimated black bear populations. Of fatal attacks, 86% (54 of 63, 1.08/yr) occurred between 1960 and 2009. There was positive linear relationship between the number of fatal black bear attacks per decade and human population size in the United States and Canada per decade (r2 = 0.92, β = 0.000, P ≤ 0.001). Of fatal attacks, 91% (49 of 54) occurred on parties of 1 or 2 persons. In 38% (15 of 40) of incidents, peoples' food or garbage probably influenced the bear being in the attack location. We judged that the bear involved acted as a predator in 88% (49 of 56) of fatal incidents. Adult (n = 23) or subadult (n = 10) male bears were involved in 92% (33 of 36) of fatal predatory incidents, reflecting biological and behavioral differences between male and female bears. That most fatal black bear attacks were predatory and were carried out by 1 bear shows that females with young are not the most dangerous black bears. As a result of our research agencies managing black bear can more accurately understand the risk of being killed by a black bear, and can communicate this to the public. With training, people can learn to recognize the behaviors of a bear considering them as prey and can act to deter predation. © 2011 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".