Evaluation of a Rule-Based Decision Aid for Recreational Travelers in Avalanche Terrain
Bibliographic record
Abstract
ABSTRACT: This paper evaluates a new decision aid for traveling in avalanche terrain. The aid is intended primarily for winter recreationists in Canada and provides guidance in trip selection, route finding and slope evaluation. In contrast to other avalanche decision aids, this tool does not attempt to calculate risk or the probability of triggering an avalanche. Instead, it frames alternatives in terms of prevention value, or the portion of historical accidents that would have been prevented had the victims used the thresholds of the aid as decision crite-ria. This paper examines the two components that comprise the decision aid. The first com-ponent, the Avaluator Trip Planner, is quantitatively evaluated using 21 years of Canadian avalanche accident data. The second component, the Obvious Clues Method, is quantita-tively evaluated for application in Canada by building on a previous analysis of its effective-ness in the United States. Combined, the two components offer a prevention value of over 90 % of historical Canadian avalanche accidents. Type I errors by the decision aid (false negative results) are most likely to occur under moderate danger rating and involve small iso-lated slabs or deep instabilities. The paper concludes by considering the possible impact of the decision aid on future accident trends, and shows that it may be possible to detect a re-duction in Canadian avalanche accidents in as little as three to four seasons after recreation-ists adopt the decision aid.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".