An Investigation of Factors Influencing Bear Spray Performance
Bibliographic record
Abstract
ABSTRACT Several studies have documented the effectiveness of bear spray in protecting users from aggressive bears. Bear spray failures, however, have also been reported along with speculation regarding the influences of temperature, wind, repeated canister use, and canister age on spray efficacy. We designed lab and field experiments to document the influence that temperature, wind, repeated discharges from the same canister, and canister age have on bear spray performance. To determine the influence of temperature on spray performance, we recorded canister head pressures at temperatures ranging from −23°C to 25°C and found a strong, positive linear relationship. Even at the lowest temperature tested (−23°C), bear spray had a range >4 m, though the plume was narrow and the spray was not well aerosolized. As canister temperature increased, head pressure, plume distance, and dispersion increased. We used computational fluid dynamics modeling and simulated the effect that headwinds, crosswinds, and tailwinds of varying speeds had on spray performance. Even under high headwind and crosswind scenarios (>10 m/sec), sprays reached targets that were approximately 2 m directly in front of the user. Crosswinds affected spray plume distance similar to headwinds, but the effect was not as pronounced. Tailwinds improved spray performance with respect to speed and distance. By weighing unused canisters ≤18 years old, brands tested lost weight ranging from 0.65 g/year to 1.92 g/year, presumably because of propellant that escaped canister seals. We also documented that bear spray head pressure declines in a logarithmic, not linear, fashion; over half of a new (7‐sec spray time) canister's pressure was lost in the first 1 second of spray. We recommend not test‐firing cans, keeping cans warm when in the cold, and retiring them when ≥4 years of age. Our results provide no compelling reason to not carry bear spray in all areas where bears occur, even if it is windy or cold. © 2020 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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".