Linking avalanche problem types to modelled weather and snowpack conditions: A pilot study in Glacier National Park, British Columbia
Bibliographic record
Abstract
To help amateur recreationists to make better informed decisions about when and where to travel in the backcountry, Canadian avalanche bulletins include structured information on the nature of avalanche problems of concern. Using conditional inference trees, this study explores the relationships between modelled weather and snowpack conditions and avalanche problems identified by forecasters in Glacier Nation Park, British Columbia, during the 2013 to 2018 winter seasons to better understand what makes avalanche forecasters identify individual avalanche problem types and explore possibilities for predicting avalanche problems in data-spare regions using numerical models. The results confirm the influence of the expected weather and snowpack variables and provide useful additional insight into forecaster practices when making decisions about avalanche problems. This study provides an important step for integrating avalanche problems and the Conceptual Model of Avalanche Hazard into existing weather and snowpack model chains and making avalanche bulletins in Canada more consistent.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".