Fracture toughness of dry snow slab avalanches from field measurements
Bibliographic record
Abstract
Dry snow slab avalanches release by propagating fractures. The first fracture occurs in shear within a thin weak layer underneath a planar slab which eventually fails in tension perpendicular to the weak layer at some distance up‐slope. Thus fracture properties of both the weak layer (in shear) and the slab (in tension) are important for determining the character of the slab avalanche including its volume and, hence, destructive potential. In this paper, the fundamental fracture properties relevant during slab avalanche release are evaluated for both weak layer shear and slab tensile fracture from field measurements. Data from nearly 300 slab avalanches are used to estimate tensile and shear fracture toughness. Two important practical results come from the analysis. The first is that, on average, slab tensile toughness is about 5–7 times the weak layer shear toughness with a range of about 2–15. This is of immense practical importance since it allows estimates of slab dimensions (length) and width from measured estimates of the slab depth, D. The second important result is that a length scale is provided for a highly stressed (tensile stress) boundary layer of about 20 cm from the weak layer up through the body of the slab over which the tensile crack should first form. This estimate gives an important scale for field evaluation of snow slab instability for measurements from snow profiles. For example, it gives a guideline for estimating important hardness changes between the weak layer and the slab which have been shown from field data to be associated with human triggering of snow slabs. We expect that our results will provide a framework for analysis of rock avalanches and flake‐type landslides which fail on weak interfaces under slabs.
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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 | 0.002 | 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".