Simple calculations of avalanche risk for backcountry skiing
Bibliographic record
Abstract
ABSTRACT: The avalanche risk from backcountry skiing and its dependence on the regional danger ratings have not been estimated—notably in North America. To get around this data gap, we have tried to calculate the risk using an event tree which breaks the avalanche risk to backcountry skiers into the probability of triggering, the probability of being caught in a triggered avalanche, and the probability of death if caught. An expert survey estimated the probability of triggering a potentially fatal avalanche while making fresh tracks in a trigger zone for each level of regional avalanche danger. A previous study found that 40 % of people who trigger a potentially fatal avalanche are caught. Based on accident reports, the probability of being killed depends on the avalanche size, the distribution of which is known for skier-triggered avalanches in Canada. To calculate the daily risk, we modelled making fresh tracks in a specified number of trigger zones as an encounter probability. Based on our assumptions, a person can directly ski a dozen or so trigger zones in a day while the regional danger is Low or Moderate with comparable risk to kayaking. For a person skiing directly 5-10 trigger zones while the avalanche danger is Considerable, the daily risk is likely higher than kayaking and in the range of rock climbing or mountaineering.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".