Exploring the link between the Conceptual Model of Avalanche Hazard and the North American Public Avalanche Danger Scale
Bibliographic record
Abstract
In 2010, Statham, Haegeli, et al. (2018) introduced the Conceptual Model of Avalanche Hazard (CMAH) to improve transparency and consistency of avalanche bulletin production in North America.However, since the CMAH has no explicit link to the avalanche danger scale, forecasters must rely on their own judgment to assign danger ratings, which can lead to inconsistencies in public avalanche risk communication.My research aims to address this missing link by exploring the relationship between avalanche hazard assessments and danger rating assignments in public avalanche bulletins.Using conditional inference trees, key decision rules and components of the CMAH influencing danger rating assignments are extracted.While the analysis offers insights into the assignment rules, it also highlights substantial variability that cannot be explained by components of the CMAH.The results from this study offer a foundation for critically reviewing existing forecasting practices and developing evidence-based decision aids to increase danger rating consistency.
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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 | 0.013 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| 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".