The Avaluator – A Canadian Rule-Based Avalanche Decision Support Tool for Amateur Recreationists
Bibliographic record
Abstract
ABSTRACT: An exceptionally high number of avalanche fatalities during the winter of 2003 forced the Canadian avalanche community to question the effectiveness of existing public ava-lanche safety programs in Canada. In response to the recommendations of several avalanche safety reviews, the Canadian Avalanche Association launched the ADFAR (Avalanche Decision Framework for Amateur Recreationists) Project for the development of a practical, science-based decision framework for amateur recreationists when planning for, or traveling in avalanche terrain. The goal of the project was to reduce recreational avalanche fatalities by improving risk commu-nication and risk awareness among the fast growing number of winter backcountry enthusiasts in Canada. The Avaluator is a new rule-based decision support tool for amateur recreationists, including backcountry skiers and snowboarders, snowmobile riders and out-of-bounds skiers and snow-boarders. A key part of the Avaluator is a pocket card that assists with planning backcountry trips and facilitates field decisions. The paper provides an overview of the ADFAR project, describes the usage of the Avaluator and discusses the underlying design principles.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.005 |
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".