EVALUATION OF THE AVALUATOR DECISION-SUPPORT TOOL FOR CANADIAN ACCIDENTS: 1997-2009
Bibliographic record
Abstract
The Avaluator™ is a rule-based avalanche decision-support tool for amateur backcountry recreationists, published by the Canadian Avalanche Centre. It consists of a Trip Planner (TP) for choosing appropriate backcountry destinations, and a slope assessment tool called the 'Obvious Clues Method'© (OCM) for use in the field. Evaluating a decision aid with historic avalanche accident records is crucial for assessing its effectiveness. While the TP component of the Avaluator was examined with respect to Canadian accidents during its development, the OCM component was validated using only U.S. accident data. The goal of the current study is to provide the first evaluation of the Avaluator™ using only Canadian accident data. Significant effort was made to compile a complete record for each fatal avalanche accident that occurred in Canada in the seasons 1997 to 2009; however, missing data remain a significant challenge in the evaluation. Unfortunately, no simple and consistent treatment was available to handle missing data in the analysis. Therefore, accident prevention values were calculated under several assumptions regarding missing data to provide insights on the limits of possible values, and allow the direct comparison with values calculated from the U.S. data. The analysis showed that clue presence in Canadian accidents was not significantly different from that published in the Avaluator™, although the Avaluator™ values may be similar to the upper limit for the Canadian dataset. The main conclusion of this study is that further investigation of each accident record would reduce missing data, and allow a much more reliable evaluation.
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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 | 0.008 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".