Fatal Avalanche Accidents and Forecasted Danger Levels: Patterns in the United States, Canada, Switzerland and France
Bibliographic record
Abstract
Throughout the winter, avalanche forecasters issue bulletins to help the public and the managers of\npublic facilities make avalanche safety decisions. These bulletins typically describe important snowpack\nfeatures and current weather events before rating the avalanche danger on a scale of one through five.\nAlthough the character of avalanche conditions may vary between regions, the physical processes that\nform avalanches are universal. In addition, the methods used to forecast avalanche activity are similar\nthroughout North America and Europe. We use the distribution of fatal avalanche accidents with respect\nto forecasted avalanche danger level to examine how effectively avalanche forecast groups\ncommunicate with the public and how consistent these groups are within countries and internationally.\nThe results show that avalanche forecast groups are effectively communicating with the public when\nrelatively benign or very dangerous conditions exist.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".