Meteorological, elevation, and slope effects on surface hoar formation
Bibliographic record
Abstract
Abstract. Failure in layers of buried surface hoar crystals (frost) can cause hazardous snow slab avalanches. Surface hoar crystals form on the snow surface and are sensitive to micro-meteorological conditions. In this study, the role of meteorological and terrain factors were investigated for three surface hoar layers in the Columbia Mountains of Canada. The distribution of crystals was observed over different elevations and aspects during 20 days of field observations. The same layers were modelled on a 2.5 km horizontal grid by forcing the snow cover model SNOWPACK with forecast weather data from a numerical weather prediction model. The moisture content of the air (i.e. absolute humidity) had the largest impact on modelled surface hoar growth, with warm and moist air being favourable. Surface hoar was most developed at certain elevation bands, usually corresponding to elevations with warm humid air, light winds, and cold surface temperatures. SNOWPACK simulations on virtual slopes systematically predicted smaller surface hoar on south-facing slopes. In the field, a complex combination of surface hoar and sun crusts were observed, suggesting the model did not adequately resolve the surface energy balance on slopes. Overall, a coupled weather–snow cover model could benefit avalanche forecasters by predicting surface hoar layers on a regional scale over different elevation bands.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".