Evaluating the Avaluator Avalanche Accident Prevention Card 2.0
Bibliographic record
Abstract
consisting of the Trip Planner and Obvious Clues, was marketed by the Canadian Avalanche Association/Center (CAA/C) as a decision support tool for prevention of avalanche accidents. For Obvious Clues, users simply added up the number of obvious clues (e.g., loading, terrain trap) and the Avaluator provided them with the percentage of accidents prevented and travel recommendations. However, the Avaluator's prevention values differed widely from the values reported by Haegeli and McCammon previously (Uttl et al., 2007, 2008) and were not replicated by several independent studies (Uttl et al., 2008, 2009; Floyer, 2008). Moreover, Haegeli and McCammon refused all requests to produce the data behind their claims and for clarification of their methodology (Uttl et al., 2008, 2009). The CAA/C advised Avalanche Safety Training instructors not to use the Obvious Clues prevention values (Calgary Herald, April 20, 2009); included new disclaimers absolving the authors and CAA/C of any responsibility for any deaths, injuries and other damages caused by the Avaluator; and eventually, published the Avaluator 2.0 (Haegeli, 2010) with the Obvious Clues replaced by “Slope Evaluation ” and McCammon no longer appearing as one of the authors. We examined whether the new Slope Evaluation is likely to prevent more or fewer accidents than the original Avaluator. Our analysis of over 1,000 North American accidents suggest that the Avaluator 2.0 suffers from many of the same problems that plagued the original Avaluator. We discuss implications of our findings for avalanche safety training programs.
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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 | 0.009 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".