Exploring the link between the Conceptual Model of Avalanche Hazard and the North American Public Avalanche Danger Scale
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it