Modeling Crack Arrest in Snow Slab Avalanches—Toward Estimating Avalanche Release Sizes
Bibliographic record
Abstract
Abstract Dry‐snow slab avalanches are the most fatal type of avalanches, beginning with the failure of a weak snow layer below cohesive slabs. This failure can propagate within the weak layer, causing the overlying slab to fracture and slide. Avalanche forecasters are interested in evaluating crack propagation propensity and potential avalanche sizes. This study tests the hypothesis that two factors may stop dynamic crack propagation: snowpack heterogeneity and terrain variations. We develop a depth‐averaged Material Point Method, which combines MPM with shallow water assumptions for efficient elastic‐brittle modeling of avalanche release. We analyze two scenarios: pure‐elastic and brittle slabs. In the pure‐elastic case, we observe a significant decrease in slab tensile stress with increasing crack speed and provide an analytical formulation for this phenomenon. We evaluate the impacts of weak layer heterogeneity and fracture energy on crack stopping. In the brittle scenario, we explore the interaction between weak layer heterogeneity and slab fracture, quantifying their combined effects on crack arrest. Our results reveal a scaling law that relates crack arrest distance to dimensionless numbers indicative of weak layer and slab strength. The model is applied in case studies to predict release sizes based on field data, and also on synthetic 3D topographies, enhancing the understanding of factors influencing avalanche size and aiding future mitigation strategies.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".