Efficacy of bear spray as a deterrent against polar bears
Bibliographic record
Abstract
Abstract Although there have been few attempts to systematically analyze information on the use of deterrents on polar bears ( Ursus maritimus ), understanding their effectiveness in mitigating human‐polar bear conflicts is critical to ensuring both human safety and polar bear conservation. To fill this knowledge gap, we analyzed 19 incidents involving the use of bear spray on free‐ranging polar bears from 1986 to 2019 in Canada, Russia, and the United States to evaluate the effectiveness of bear spray as a polar bear deterrent. We found that bear spray was an effective deterrent in close‐range encounters with polar bears, stopping undesirable behavior in 18 of 19 incidents. Bear spray effectively deterred both curious and aggressive polar bears, including polar bears attempting to attack people. The mean distance between user and bear at the time of spraying was 2 m (min–max = 0.2–10.0 m, mode = 1 m), though bears were usually first seen at greater distances. Bear spray was successfully deployed against polar bears in all 4 seasons. Wind affected spray performance in 1 of 19 of incidents. In 8 of 14 bear spray incidents, other deterrents were used without success before bear spray was used effectively to deter polar bears. No humans or polar bears were killed or injured in any of the incidents in which bear spray was used. We also analyzed 54 polar bear attacks and attempted attacks on humans where bear spray was not carried. The data suggest that in 93% of those incidents, the use of bear spray might have saved the lives of both the people and bears involved if it had been available and used. Our analysis improves our understanding of the effectiveness of bear spray for polar bear conflict mitigation.
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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 | 0.001 | 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) | 0.009 | 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".