IMPROVEMENT OF SNOW GRAIN SIMULATIONS FROM THE MULTI-LAYERED THERMODYNAMIC SNOW MODEL SNOWPACK: IMPLICATIONS TO AVALANCHE RISK ASSESSMENT
Bibliographic record
Abstract
The snow thermodynamic multi-layers model SNOWPACK was developed by the Swiss Federal Research Institute (WSL/SLF) in Switzerland in order to address the risk of avalanches by simu- lating the vertical geophysical and thermophysical properties of snow. SNOWPACK risk assessments are based on the simulation of snow microstructure (i.e. grain size, sphericity, dendricity and bond size). Pre- vious research has shown a systematic bias in the grain size simulations (equivalent optical grain size) over several areas in northern Canada. Snow specific surface area (S SA), a grain size metric, was meas- ured using a laser-based system measuring snow albedo through an integrating sphere (InfraRed Inte- grating Sphere, IRIS) at 1310 nm. Optical grain size was retrieved from the SSA measurements in order to be compared with the optical equivalent snow grain radius from SNOWPACK outputs. A field campaign was conducted during the 2014 winter in the Canadian Rockies to validate the bias and. Three study plots were selected, each with its own climate particularities. The first site was located at Mt. Fidelity in Glacier National Park, BC. The second site was located within the Marmot Basin ski resort in Jasper National Park, AB and finally, the third site is located in Reserve naturelle des Chic-Chocs, QC. Profiles of snow tem perature, density, grain size (IRIS) were conducted, and stratigraphic analysis completed using visual interpretation, combine with a snow micropenetrometer (SMP). The measurements are expected to pro- vide detailed information on snow microstructure, leading to a snow grain correction coefficient for SNOWPACK for the improvement of snow stability predictions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".