Modeling snow instability with the snow-cover model SNOWPACK
Bibliographic record
Abstract
Abstract SNOWPACK has been in operational use for five consecutive winters on approximately 100 automatic weather stations in the Swiss Alps. It calculates snow precipitation, snowdrift and the layered structure of the snow cover. An analysis routine has been implemented that gives a stability estimation for a model profile. We distinguish between slab instability and direct action or deformation-rate instability. Slab instability relies on a static force balance within the snowpack (stability index) and may be used to assess stability for both natural and skier-triggered slab avalanches. We heuristically improve the slab index by adding a term of overload correction for all grain types and scaling the stability index with the bond size. Deformation-rate instability means that the load of the snow cover increases faster than the snow gains strength. An index is formulated based on the snow deformation rate. It may be associated with large snowfall events and wet-snow situations as they occur in catastrophic situations, or with the effect of a sudden increase in temperature. The results of both stability indices are compared to the fore-casted avalanche danger. The indices are able to recognize cases of avalanching. It is shown that the inclusion of several locations, for which the indices are calculated, improves the correlation between stability indices and avalanche danger. A sufficient number of profiles could bridge the gap between snow-cover characteristics at a point and avalanche danger.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".