Trend Analysis of Canadian Avalanche Accidents: The Avaluator Avalanche Accident Prevention Card Has Not Reduced the Number of Accidents
Bibliographic record
Abstract
Canadian government avalanche prevention initiative, was designed to help recreationists avoid acci-dents, and therefore, to reduce the overall number of avalanche accidents in Canada involving recre-ational users. McCammon and Haegeli (2006) argued that the Avaluator will cause a statistically de-tectable reduction in the number of avalanche accidents within 3 or 4 seasons after its adoption. However, research has revealed that (a) the data behind the Avaluator's Obvious Clues are not avail-able for inspection (Uttl, Uttl, & Henry, 2008; Floyer, 2008); (b) Haegeli and McCammon (2006) inap-propriately excluded over 1,148 avalanche accident reports from their sample due to missing values and based the prevention values on only 252 US accidents; (c) several independent studies have found the Obvious Clues prevention values published in the Avaluator to be grossly inflated (Uttl,
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.012 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".