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Record W3089774748 · doi:10.1002/jwmg.21958

An Investigation of Factors Influencing Bear Spray Performance

2020· article· en· W3089774748 on OpenAlexaff
Tom S. Smith, James M. Wilder, Geoffrey S. York, Martyn E. Obbard, Blake W. Billings

Bibliographic record

VenueJournal of Wildlife Management · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicRangeland and Wildlife Management
Canadian institutionsTrent UniversityMinistry of Natural Resources and Forestry
Fundersnot available
KeywordsCrosswindPlumeEnvironmental scienceHead (geology)Spray characteristicsRange (aeronautics)PropellantAtmospheric sciencesMeteorologyMaterials scienceSpray nozzleNozzleEngineeringGeologyComposite materialAerospace engineeringGeography

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.016
GPT teacher head0.214
Teacher spread0.198 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2020
Admission routes1
Has abstractyes

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