HUMAN FACTORS IN AVALANCHE AVOIDANCE AND SURVIVAL: CONSEQUENCES OF VIOLATING THE RULES OF SAFE TRAVEL
Bibliographic record
Abstract
Using hundreds of historical avalanche accident records from USA and Canada, we examined human factors - avalanche victims' behavior prior to, during, and after an avalanche - which may contribute to the occurrence of avalanche accidents and serious adverse outcomes (e.g., injuries and deaths). Each avalanche accident was coded for the presence or absence of human factors influential in avalanche avoidance (e.g., more than one person on the slope, insufficient spacing, standing exposed, travel alone, familiarity with terrain) and survival (e.g., failure to carry beacons, probes, or shovels). In addition, we coded the number of descriptive parameters for each accident including participants (e.g., number of males and females), activity (e.g., skiing, snowmobiling), avalanche safety training level, and number of participants caught, injured, or killed. The results showed that a large proportion of accident victims violated some basic rules of safe travel in avalanche terrain, e.g., more than one person traveling across the slope simultaneously and insufficient spacing between participants. These violations were common in both commercially-led groups and self-guided recreational groups. A substantial number of victims also traveled alone, and therefore, rescuers were unavailable following burial by an avalanche. In sum, our results show that the number of avalanche accident injuries and death can be substantially reduced if avalanche safety training courses focus more attention on highlighting the importance of human factors in causing avalanche accident deaths and reducing survival.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".