On Estimating Avalanche Danger From Simulated Snow Profiles
Bibliographic record
Abstract
Estimating avalanche danger is the primary goal of avalanche warning services. Typically avalanche danger is estimated based on a variety of information such as manual snow profiles, avalanche observations as well as weather data. However, this required information is often not available especially in data sparse areas, which are common in Canada. It has been shown that coupled snow cover and numerical weather prediction models can provide such information on the snow cover. For this study we simulated the snow cover for three elevation bands – alpine, tree-line, below tree-line – at Glacier National Park, B.C., Canada for the winter season 2012-2013 between December and March. Snow cover simulations were performed using the Swiss snow cover model SNOWPACK forced by weather data from the Canadian high-resolution numeric weather prediction model GEM-LAM. Experienced forecasters estimated the regional avalanche danger (Low to Extreme) daily during the same period for the three elevation bands. Multivariate classification trees were used to estimate the avalanche danger from the simulated profiles. Classification trees were built using four parameters derived from the simulated profiles. These four parameters were the new snow amounts – maximum over 24-hours and 3-days – as well as measures for the likelihood of triggering and the expected avalanche size – based on a skier stability index and the depth of a critical layer. A comparison of the avalanche danger estimated from the simulated profiles with the forecasted avalanche danger showed that the avalanche danger was estimated correctly with an accuracy of 77% for the alpine, 76% for tree-line and 70% below tree-line – overall accuracy about 74%. Although the simulated avalanche danger tends to be slightly underestimated, especially for the alpine and treeline, such a model chain can be a valuable tool for avalanche warning services especially for data sparse areas.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".