The critical size of macroscopic imperfections in dry snow slab avalanche initiation
Bibliographic record
Abstract
[1] Dry snow slab avalanches initiate after mode II fracture propagation within a thin, weak layer under a planar slab. Dry alpine snow in which avalanches form is a porous material, typically with volume fraction filled by solids of between 10% and 50%. If snow slab avalanches were caused by small scale flaws, there would be continuous avalanches and alpine snow would not survive on steep slopes. Instead, snow slab avalanches initiate from macroscopic imperfections or weak zones within the weak layer. These are called “sweet spots” by practitioners and deficit zones in the pioneering work by Conway and Abrahamson in the 1980s. In this paper, the results of 750 in situ shear fracture tests from 68 slab–weak layer combinations are summarized in relation to the critical sweet spot length and weak layer crystal form. The results show that the critical sweet spot lengths follow a gamma probability density function with a range from 0.14 to 1.3 m and most probable value 0.5 m. The results also imply that critical sweet spot length depends on weak layer grain type with significantly larger values for persistent forms (surface hoar and facets) than for nonpersistent forms (decomposing and fragmented grains).
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| 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".