VULNERABILITY: CAUGHT IN AN AVALANCHE - THEN WHAT ARE THE ODDS?
Bibliographic record
Abstract
Vulnerability is an essential component in qualitative and quantitative avalanche risk analyses. It is the probable consequences given that the element-at-risk is hit by or caught in an avalanche. Since consequences vary with avalanche characteristics, there is a level of vulnerability associated with each type or size of avalanche. The avalanche size classification based on destructive potential is well suited to classifying vulnerability into different levels. We review vulnerability for vehicles on roads, buildings as well as backcountry recreationists and workers. Quantitative vulnerability typically requires some data, although expert estimation can be used with or without data. Quantitative vulnerability has the advantage that it can be used in comparisons with other risks to determine if a risk is acceptable. For backcountry recreation, data from non-fatal injuries are limited, so most calculations of vulnerability for people use only the expected probability of death. Using Canadian accident data, we estimate the vulnerability (probability of death) to roughly 0.004 to 0.007 for a Size D2 avalanche (destructive scale) and ten times higher for a Size D3 avalanche. We show how balloon packs can change the vulnerability of recreationists, and include an example of how vulnerability can be used in an avalanche risk assessment for a worksite.
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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 | 0.005 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.010 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".