Bibliographic record
Abstract
Dry slab avalanches release by a sequence of propagating fractures. In this paper, I provide field measurements of the important length parameters resulting from fractures for hundreds fallen slabs including depth, length, and width. The field data show wide variations in these parameters. Probability plots of all dimensions (length, width, and depth) suggest they approximately obey log normal probability density functions. Given slab dimensions, two applications are considered on the basis of the field data. These applications are: (1) Estimates of total fracture energy consumed around the perimeter of the slab are given, where it is presumed fracture is mostly mode I, and along the base (weak layer) of the slab, where shear fracture (mode II and III) is expected. For average characteristic dimensions, the analysis suggests that energy consumed on the perimeter is somewhat less than in the weak layer. Even though fracture energy around the perimeter is expected to be higher than in the weak layer, the larger area fractured in shear at the base of the slab compared to area fractured in tension around the perimeter results in a comparable amount of total energy needed around the perimeter. (2) Approximate estimates of avalanche mass for average characteristic dimensions based on slab depth D (the only length possibly known prior to avalanching) are made. The mass is considered related to destructive potential and simple guidelines are given to estimate mass in relation to D with validation by considering concurrent size and estimates of D by mountain guides.
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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 | 0.000 |
| 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".