Avalanche risk during backcountry skiing-- an analysis of risk factors, Natural Hazards
Bibliographic record
Abstract
Abstract. Skier-triggered avalanches are the main cause of avalanche accidents in back-country skiing. The risk of accidents during backcountry skiing was analysed statistically and related to factors such as elevation level, aspect, stability rating and the time of the year. The analysis is based on a database about terrain usage and avalanche accidents from a large heli-skiing operator in Canada, which makes it possible to study the conditional probability of accidents given the recorded pattern of terrain usage. This study shows that the historical risk of accidentally triggering an avalanche greater than size 1 depends highly on the stability rating, with the highest risk occurring during ‘‘poor’ ’ stability. The risk is greater at high elevations, and it is lower during the late season than earlier on. Skier risk does not depend as much on aspect as may be indicated from avalanche data alone. However, it is relatively high in the N–NE–E sector. These factors are not independent of each other and therefore analyses of combined factors were also performed. Questionnaires and interviews were used to gain knowledge about the terrain selection of professional mountain guides. These results indicate that when selecting terrain, guides first look at the overall shape and size of the terrain, but avalanche history of terrain and inclination are also important factors. Finally, remarks in avalanche reports were analysed, and common human factors identified. Key words: avalanche risk management, human triggered avalanches, risk analysis, avalanche
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".