Avaluator's Obvious Clues Prevention Values Are Inflated: Evidence From Canadian Avalanche Accidents
Bibliographic record
Abstract
The Avaluator Avalanche Accident Prevention Card (Haegeli & McCammon, 2006) was designed to help recreationists to avoid avalanche accidents, and therefore, reduce the overall num- ber of avalanche accidents in involving recreationists. It consists of two parts - the Trip Plan- ner and Obvious Clues -- and is marketed by the Canadian Avalanche Center as a made in Canada science decision tool. However, the research has revealed that (a) the data behind the Avalu- ator's Obvious Clues are not available for inspection (Haegeli and McCammon has repeatedly refused to provide access to their data) (Uttl, Uttl, & Henry, 2008a; Floyer, 2008), (b) Haegeli and McCammon (2006) inappropriately excluded over 1,148 avalanche accident reports from their sample due to miss- ing values and based the prevention values on only 252 accidents; (c) several independent studies found that the Obvious Clues prevention values published in the Avaluator are grossly inflated (e.g., Uttl, Henry, & Uttl, 2008b; Floyer, 2008). Moreover, the Obvious Clues prevention values published in the Avalauator are based on only US rather than Canadian accidents. Our study examined for the first time prevention values (i.e., risk reduction values) of the Obvious Clues in a sample of Canadian ava- lanche accidents. Our results show that the prevention values published in the Avaluator are grossly inflated, falsely informing users that slopes they are about to cross are relatively safe.
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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 | 0.007 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.012 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".