Evaluation of the shear frame test for weak snowpack layers
Why this work is in the frame
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Bibliographic record
Abstract
Abstract The shear frame allows testing of thin weak snowpack layers that are often critical for slab avalanche release. A shear metal frame with an area of 0.01–0.05 m 2 is used to grip the snow a few mm above a buried weak snowpack layer. Using a force gauge, the frame is pulled until a fracture occurs in the weak layer within 1 s. The strength is calculated from the maximum force divided by the area of the frame. Finite-element studies show that the shear stress in the weak layer is concentrated below the cross-members that subdivide the frame and where the weak layer is notched at the front and back of the frame. Placing the bottom of the frame in the weak layer increases the stress concentrations, and results in significantly lower strength measurements than placing the bottom of the frame a few mm above the weak layer. Based on over 800 sets of 7–12 tests in western Canada, coefficients of variation average 14% and 18% from level study plots and avalanche start zones, respectively. Consequently,sets of 12 tests typically yield a precision of the mean of 10% with 95% confidence, which is sufficient for monitoring of strength change of weak layers over time in study plots. With consistent technique, there is no significant difference in mean strength measurements obtained by different experienced shear frame operators using the same approximate loading rate and technique for placing the frame. Although fracture surfaces are usually planar, only one of eleven shapes of non-planar fracture surfaces showed significantly different strength compared to planar fracture surfaces. For weak layers thick enough for density measurements, the shear strength is plotted against density and grain form. From these data, empirical equations are determined to estimate the shear strength of weak snowpack layers.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 it