Spatial predictions of surface hoar and crust formation
Bibliographic record
Abstract
ABSTRACT: Understanding the distribution of critical snowpack layers is important when assessing avalanche hazard. Two common critical layers, surface hoar and melt-freeze crusts, form under specif-ic weather conditions. This study explores the possibility of modelling the formation of these layers with forecasted weather data. Surface hoar and sun crusts were tracked at study sites on two moun-tains in the Columbia Mountains of Canada. Weather data from automated stations near these sites were compared to forecast data from two numeric weather prediction (NWP) models (15 and 2.5 km grids). The latent heat flux and net shortwave radiation were modelled with the snow cover model SNOWPACK and related to observed surface hoar crystal size and sun crust thickness. Surface hoar formation was then predicted across western Canada with NWP data. Comparing these predictions with observations made by avalanche professionals at 112 study plots found that surface hoar occur-rence was generally over-predicted. Spatial predictions with forecast data could help avalanche fore-casting in 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 | 0.000 | 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 | 0.000 | 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".