Bibliographic record
Abstract
Abstract. Snow avalanches represent a natural hazard for infrastructures and backcountry recreationists. Risk assessment of avalanche danger is difficult due to the sparse nature of available observations informing on snowpack mechanical and geophysical properties and overall stability. The spatial variability of these properties also adds complexity to the decision-making and route finding in avalanche terrain for mountain users. Snow cover models simulate snow mechanical properties with good accuracy at fairly good spatial resolution (around 100 m). However, monitoring small-scale variability at the slope scale (5–50 m) remains critical given that slope stability and the possible size of an avalanche are governed by such scale. In order to better understand and predict the spatial variability at the slope scale, this work explores existing linkages between snow mechanical properties and microtopographic indicators. First, we compared covariance models and scaling properties. Then, we predicted snow mechanical properties, including point snow stability, using GAM spatial models (Generalized Additives Models) with microtopographic indicators as covariates. Snow mechanical properties such as snow density, elastic modulus, shear modulus and snow microstructural strength were measured at multiple locations over several studied slopes using a high-resolution snow penetrometer (SMP), in Rogers Pass, British-Columbia, and Mt Albert, Québec. Point snow stability such as the skier crack length, critical propagation crack length and a skier stability index were derived using the snow mechanical properties from SMP measurements. Microtopographic indicators such as the topographic position index (TPI), vegetation height and proximity, Up-wind slope index (wind exposed/sheltered area) and potential radiation index were derived from Unmanned Aerial Vehicles (UAV) surveys with sub-meter resolution. We computed the variogram and log-log variogram of snow mechanical properties. The comparison showed some similarities in correlation distances fractal dimensions between the slab depth and slab snow density and also between the weak layer microstructural strength and the stability metrics. GAM models suggested several significant covariates such as TPI, VRM, Winstral index, vegetation height and distance to vegetation. The point snow stability maps generated represents good teaching material in avalanche skill training and awareness course. The difference in spatial pattern between the slab and the weak layer should be considered in snow mechanical modeling.
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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 | 0.003 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.030 | 0.026 |
| Insufficient payload (model declined to judge) | 0.153 | 0.118 |
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".