Study Brief: Decision Making by Amateur Winter Recreationists in Avalanche Terrain
Bibliographic record
Abstract
Over the past 10 winter seasons (1995/96 – 2004/05), backcountry recreationists have accounted for 89 % of all avalanche fatalities in Canada. Of the 127 recreational fatalities, 65 were backcountry skiers, 33 snowmobile riders, and 12 out-of-bounds skiers. Furthermore, 63% of all fatal avalanche accidents occur within British Columbia (Canadian Avalanche Centre, 2005). Avalanche professionals use established knowledge-based methods for comprehensively evaluating avalanche hazards. The proper use of these methods requires extensive training, practical experience and a thorough understanding of the avalanche phenomenon. Because amateur recreationists generally spent considerably less time in avalanche terrain, they are normally not able to achieve a similar level of expertise. As a consequence, professional methods are too complicated and the recommendations too vague for assisting amateur recreationists in recognizing and responding to dangerous avalanche conditions. To address the need for practical decision aids for amateur recreationists, a number of rule-based decision frameworks were introduced in Europe during the last decade (e.g., Reduction Method, SnowCard, Stop-or-Go, Nivotest; McCammon and Hägeli, 2005). The tragic winter of 2002/03, where Canada experienced an exceptional high number of recreational avalanche fatalities, prompted the Canadian avalanche community to plan a project for the design of a practical, science-based decision frameworks tailored to the specific avalanche conditions and dominant backcountry recreational user groups in Western Canada. The resulting ADFAR project (Avalanche Decision-Making Framework for Amateur Recreationists) is administered by the Canadian Avalanche Association (CAA) and is sponsored by Parks Canada with a grant from the
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".