A decision support tool for dry persistent deep slab avalanches for the transitional snow climate of western Canada
Bibliographic record
Abstract
A decision support tool to aid in forecasting the likelihood of dry persistent deep slab avalanches was created from three separate data sources in western Canada. Data were obtained from an expert opinion survey of avalanche professionals, a dataset of avalanched starting zones that were field-investigated, and a dataset of avalanches from the Canadian information sharing system. The survey and the tool consisted of three sections: snowpack conditions, weather conditions, and avalanche observations. Parameters in the tool were assigned importance values derived from the survey responses. A classification tree was used to determine the threshold tool sum for increasing the likelihood of observing natural persistent deep slab avalanches. Based on some of the data used to create the tool, the tool correctly explained 75% of days with natural avalanches (16 out of 18) and non-avalanche days (61 out of 85), but the false alarm ratio was high (60%). The tool also indicates if triggered avalanches from localized dynamic loads are possible, depending on responses in the snowpack conditions section of the tool. Avalanche forecasters must apply the tool to certain terrain characteristics, at a local to regional scale. The tool may benefit from location-based calibration. The tool only indicates the likelihood of persistent deep slab avalanches based on the datasets used and it cannot determine when or where they will occur.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".