The Effect of Slab and Bed Surface Stiffness on the Skier-Induced Shear Stress in Weak Snowpack Layers
Bibliographic record
Abstract
A finite element model was developed to simulate a snowpack with localized surface loading\nof a skier on a slab of varying stiffness overlaying a thin, weak layer. The goals of this study were (1) to\ndetermine the effect of average slab thickness on stress concentrations in the underlying weak layer, and\n(2) to determine the effect of the stiffness of the slab and the bed surface under the weak layer on stress\nwithin the weak layer. The model simulates a snowpack with a slab varying from 0.1 to 0.7 m thick, and\nwith stiffness ratios between the Young’s modulus for the slab, weak layer and bed surface varying from 1\nto 25. The two-dimensional model assumes snow behaves as a linear-elastic, compressible material, and\nthat a static skier load is applied. The model results for a homogeneous snowpack had peak stresses\nwithin 2.5% of the analytical solution for a strip load. The effect of average slab stiffness on the shear\nstress within the weak layer was assessed by varying the slab stiffness from a soft slab to a stiff slab for\nvarious slab thicknesses. The peak shear stress in the weak layer was highest for the softest slab and\ndecreased with increasing slab stiffness. Stress through the snowpack decreased non-linearly with\nincreasing depth.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".