Efficacy of Bear Deterrent Spray in Alaska
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract: We present a comprehensive look at a sample of bear spray incidents that occurred in Alaska, USA, from 1985 to 2006. We analyzed 83 bear spray incidents involving brown bears ( Ursus arctos ; 61 cases, 74%), black bears ( Ursus americanus ; 20 cases, 24%), and polar bears ( Ursus maritimus ; 2 cases, 2%). Of the 72 cases where persons sprayed bears to defend themselves, 50 (69%) involved brown bears, 20 (28%) black bears, and 2 (3%) polar bears. Red pepper spray stopped bears' undesirable behavior 92% of the time when used on brown bears, 90% for black bears, and 100% for polar bears. Of all persons carrying sprays, 98% were uninjured by bears in close‐range encounters. All bear—inflicted injuries ( n = 3) associated with defensive spraying involved brown bears and were relatively minor (i.e., no hospitalization required). In 7% (5 of 71) of bear spray incidents, wind was reported to have interfered with spray accuracy, although it reached the bear in all cases. In 14% (10 of 71) of bear spray incidents, users reported the spray having had negative side effects upon themselves, ranging from minor irritation (11%, 8 of 71) to near incapacitation (3%, 2 of 71). Bear spray represents an effective alternative to lethal force and should be considered as an option for personal safety for those recreating and working in bear country. (JOURNAL OF WILDLIFE MANAGEMENT 72(3):640–645; 2008)
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.
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 | 0.002 | 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 | 0.001 | 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 it