Bibliographic record
Abstract
Dry slab avalanches release by propagation of shear fractures in a thin, planar weak layer underneath a cohesive, stronger slab. This implies that a balance between shear fracture toughness and the shear fracture stress intensity factor fundamentally determines snow slab stability. Conventionally, fracture toughness is a material property and, for most materials, emphasis is on tensile fracture since most materials fail first in tension. However, for the snow slab, the weak layer fractures first, and field measurements show that the slab material is always stronger than the weak layer. Snow slabs in nature vary with respect to mechanical properties and other characteristics, so the concept of fracture toughness as a material property has no relevance when it is calculated for different avalanches. In this paper, field measurements collected from hundreds of snow slabs are combined with the cohesive crack model to yield estimates for the mode II shear fracture toughness, KIIc. The results suggest that the snow slab has extremely low fracture toughness with variations in nature over more than two orders of magnitude. The nominal weak layer shear strength τNu has a power law relationship with respect to the fundamental scaling parameter: the slab thickness, D. Model results also imply that KIIc has power law scaling with respect to D. It is also shown that KIIc follows a log‐normal probability density function. This implies a multifractal character by considering the moments, q, of shear fracture toughness scaled with D. Positive multifractal dimensions are suggested for q up to approximately 2.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".