Efficacy of firearms for bear deterrence in Alaska
Bibliographic record
Abstract
Abstract We compiled, summarized, and reviewed 269 incidents of bear–human conflict involving firearms that occurred in Alaska during 1883–2009. Encounters involving brown bears ( Ursus arctos ; 218 incidents, 81%), black bears ( Ursus americanus ; 30 incidents, 11%), polar bears ( Ursus maritimus ; 6 incidents, 2%), and 15 (6%) unidentified species provided insight into firearms success and failure. A total of 444 people and at least 367 bears were involved in these incidents. We found no significant difference in success rates (i.e., success being when the bear was stopped in its aggressive behavior) associated with long guns (76%) and handguns (84%). Moreover, firearm bearers suffered the same injury rates in close encounters with bears whether they used their firearms or not. Bears were killed in 61% ( n = 162) of bear–firearms incidents. Additionally, we identified multiple reasons for firearms failing to stop an aggressive bear. Using logistic regression, the best model for predicting a successful outcome for firearm users included species and cohort of bear, human activity at time of encounter, whether or not the bear charged, and if fish or game meat was present. Firearm variables (e.g., type of gun, number of shots) were not useful in predicting outcomes in bear–firearms incidents. Although firearms have failed to protect some users, they are the only deterrent that can lethally stop an aggressive bear. Where firearms have failed to protect people, we identified contributing causes. Our findings suggest that only those proficient in firearms use should rely on them for protection in bear country. © 2012 The Wildlife Society.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".