Risk of Death and Major Injury from Natural Winter Hazards in Helicopter and Snowcat Skiing in Canada
Bibliographic record
Abstract
INTRODUCTION: Guests and guides partaking in helicopter and snowcat skiing (collectively known as mechanized skiing) are exposed to numerous natural winter hazards that can result in injury or death, but detailed quantitative risk estimates are currently lacking. This lack represents a considerable barrier for evaluating existing risk management practices and implementing evidence-based improvements. METHODS: We collected historical incident and exposure information from mechanized skiing operations in Canada to perform a retrospective risk analysis. Our analysis dataset includes 713 incidents that resulted in injuries or fatalities among guests or guides during a total of 3,258,000 skier days from the 1970 to 2016 winter season. RESULTS: Overall risk of death from natural winter hazards in mechanized skiing was 18.6 fatalities per million skier days (1997-2016). Although the risk of death from avalanches decreased substantially over the entire study period, avalanches remain the largest contributor to the overall risk of death (77%), followed by tree wells and other non-avalanche-related snow immersions. The risk of death from avalanches in snowcat skiing is about half of that in helicopter skiing, but other snow immersion fatalities are more common. The risk of major injury to guests is primarily associated with other falls and collisions. The risk of major injury for guides is higher in snowcat skiing than in helicopter skiing. CONCLUSION: We recommend the design of an industry-wide incident and near-miss reporting system to support evidence-based improvements of safety practices.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".