AVALANCHE ACCIDENT RISK REDUCTION TOOLS IN A NORTH AMERICAN CONTEXT
Bibliographic record
Abstract
Over the last decade, a number of avalanche risk reduction tools have been developed to assist recreational winter backcountry users avoid avalanche accidents, including the Avaluator Avalanche Accident Prevention Card (Haegeli & McCammon, 2006), Avaluator V2.0 (Haegeli, 2010), the Nivo Test (Bolognesi, 2001, 2007), and Munter's Reduction Method (Munter, 1997, 2003). These tools are based on different sets of assumptions and data, focus on widely varied pieces of information, and use different processes to arrive at their respective recommendations for safe travel. McCammon and Haegeli (2005) evaluated some of these methods using North American avalanche accident records but unfortunately they inappropriately deleted all accidents with missing data from their data set (e.g., if an accident record did not state whether or not there was any new snow, they simply deleted it) (see Uttl et al., 2008, 2009, 2010). We reviewed and evaluated these and other avalanche risk reduction tools in a North American context using over 1,000 Canadian and US avalanche accident records as well as computer simulations and modeling. We discuss the 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".