PITFALLS IN DEVELOPMENT OF AVALANCHE ACCIDENT RISK REDUCTION TOOLS
Bibliographic record
Abstract
We present a new avalanche accident tool for recreational backcountry users: the 7CF. We evaluated the new tool's effectiveness on hundreds of avalanche accident records in the USA and Canada and found that the new 7CF tool has the equivalent risk reduction (sometimes called prevention value) to the Avaluator Accident Prevention Card (Haegeli & McCammon, 2006) used in Canada (see Uttl et al., 2008a,b; Uttl et al., 2009a,b,c,d). However, the new tool requires significantly less user knowledge and training and relies only on the easily recognized clues. Thus, in comparison to the Avaluator's Obvious Clues Method (Haegeli & McCammon, 2006), the most significant advantages of the new 7CF tool include the reliability of clue detection and the ease of use. To illustrate, our research demonstrates that even users with no prior avalanche terrain experience are able to correctly recognize the presence and absence of all the 7CF clues with 99.9% accuracy. The 7CF tool is available for any interested users for free and is released under GNU General Public License, meaning that anyone is permitted to copy, change, and distribute the new tool. However, the 7CF tool is subject to some of the same limitations that plague all of the avalanche accident tools developed and evaluated using the avalanche accident records and the risk reduction strategy. We discuss some of these limitations and illustrate them using the 7CF, the Avaluator's Obvious Clues Methods, and other tools.
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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 | 0.036 | 0.166 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".