Risk Trends at U.S. and British Columbia Ski Areas: An Evaluation of the Risk of Snow Immersion Versus Avalanche Burials
Bibliographic record
Abstract
The risk trends for avalanche and snow accidents at U.S. and B.C. ski areas are changing. During the period from 1990/91 to 2005/06, there were four avalanche fatalities in bounds reflecting that that risk has been reduced significantly. Fifty-five fatal avalanche burials in the same time frame indicate that there is still a substantial risk associated with leaving the ski area boundary. What is not well documented is that during the same sixteen years, there has also been an increasing trend in the risk of asphyxiation in deep snow at ski areas. Fifty-one of these snow immersion events occurred during the study period. The classification for this type of fatality is Non-Avalanche Related Snow Immersion Death or NARSID. This risk trend appears to be “under the radar’ of many snow safety professionals. This study was designed to investigate these factors; the recognition of the risk, the key factors in the snow immersion phenomenon, and prevention strategies. Currently, the greatest single component of snow immersion risk is that it is substantially under-appreciated. The investigation included analyzing avalanche and snow immersion statistical data and designing a database of all documented snow immersion accidents. Individual cases were further researched by personal communication with ski area personnel. A field experiment using human subjects was also conducted to test factors like the effect of body position, extrication techniques, and the impact of nonreleasable snowboard bindings.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".