Linking Avalanche Problems to Avalanche Danger— A First Statistical Examination of the Conceptual Model of Avalanche Hazard
Bibliographic record
Abstract
danger scale to replace the two scales that were used in Canada and the United States since the mid 1990s. One of the most significant advancements of the new scale was the improved link to the definition of avalanche hazard, which states that avalanche hazard is a function of both the likelihood of triggering and the destructive size of avalanches (Statham, 2008). The descriptors of the danger rating levels that previously focused exclusively on likelihood of triggering were therefore augmented with additional infor-mation on the destructive size of avalanches and their distribution in the terrain. While the multi-dimensional perspective of the new danger scale more accurately reflects the true nature of avalanche hazard, it broke the well-established link between likelihood of triggering and danger rating levels that tra-ditionally provided forecasters with guidance on what danger level to choose. With the new danger scale in place, the question was: What combinations of avalanche conditions are associated with what danger level? The present study aims to address this question quantitatively by examining operational avalanche forecasting data from the Canadian Avalanche Centre (CAC). Since the winter of 2009/10, CAC forecast-ers diligently characterize avalanche conditions according to the conceptual model of avalanche hazard (Statham et al., 2010b) before specifying a danger rating. Using proportional odds logistic regression analysis, we explore the relationships between the various components of the conceptual model and ava-
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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 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".