Analysis of critical length measurements for dry snow slab weak-layer shear fracture
Bibliographic record
Abstract
Abstract Snow slab avalanches primarily release by propagation of shear fractures within thin weak layers under much thicker slabs. In some cases, the weak layer is on the order of 1 mm thick and such fractures may be considered to be a mode II shear fracture at initiation. In the cases analysed in this paper, the weak layer has finite thickness, and slope-normal effects may be present. Field data from >500 snow shear fracture tests are analysed and applied to the problem of weak-layer fracture. The paper contains a detailed analysis using a simple analytical model to estimate the critical length prior to an unstable shear fracture. The model contains the assumption of a finite fracture process zone which may be a significant fraction of the slab depth,D, or the critical length,L, for weak-layer shear fracture. The field results show that theL/Dratio varies from ∼0.1 to just over 2, and the model results are close to the same range. The analysis also shows that both the field and model results forL/Dfollow a Gumbel probability density function. Since the experimental field data contain rate-dependent (viscoelastic) and slope-normal effects, it is imperative to account for these in the model and for snow slab instability evaluation when using test data. Detailed evaluations considering both these effects are given. The applicability of the test data to avalanche release is discussed.
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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 | 0.001 | 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".