International Snow Science Workshop HUMAN RISK FACTORS IN AVALANCHE INCIDENTS
Bibliographic record
Abstract
ABSTRACT: An average of 12 people die in avalanches each year in Western Canada. The risk factors for the avalanche phenomenon have been extensively studied. The risk factors associated with the decision making process that leads individuals to expose themselves to avalanche hazard are less well understood. The recommended first step in an injury prevention program is to survey the population to discover the extent of the problem and the risk factors that predispose a person to injury. A retrospective, self-report, web-based, cross-sectional survey designed to measure potential risk factors for avalanche involvement was developed and validated. The survey was administered in September – December 2007 so as to obtain a representative sample from the population of skiers, snowboarders, climbers and snowshoers who entered avalanche terrain in Western Canada in the previous year. Back country skiers are at greater risk of experiencing an avalanche incident than out of bounds skiers or cross-country skiers and snowshoers [Odds Ratio (OR)=2.4]. Males who typically travel with other males are at greater risk than females and males who travel in mixed gender groups at least 75 % of the time (OR=2.6). Participants in the 25-29 (OR=2.6) year age range are also at greater risk than younger or older people. Attitude may have a strong association with risk of experiencing an avalanche incident (OR=6.7). KEY WORDS: Risk; attitude; injury prevention; training; perception; avalanche. 1.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.040 | 0.003 |
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".