RECENT TRENDS AND PREVENTION STATEGIES FOR AVALANCHE AND SNOW IMMERSION RISK AT U.S. AND B.C. SKI AREAS
Bibliographic record
Abstract
Many ISSW participants are involved in snow safety operations at ski areas. Understanding recent changes in trends in avalanche and snow immersion risk at ski areas will help us better educate and protect ski area users. Analysis of the last 18 years found 6 ski area avalanche fatalities in bounds reflecting a trend in reduction. 60 fatalities (12 % of the total U.S. and 9 % of Canadian total) associated with leaving the ski area boundary indicate a flat trend in this risky in this behavior. Not well documented during the same 18 years, is an increasing trend in the risk of asphyxiation in deep snow in bounds at ski areas. 57 Non-Avalanche Related Snow Immersion Deaths (NARSID) occurred during the study period. NARSID currently accounts for 15 % of all ski area fatalities in the United States. In B.C., during the period from 1993 to 1998, it accounted for 25 % of all ski area in bounds fatalities. The NARSID risk in the U.S. is currently 15 times greater than the avalanche risk in bounds at a ski area. In 2005-06 alone, 40 % of the total snowboard fatalities at U.S. ski areas were NARSIDs. The greatest risk continues to be lack of awareness.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".