Bibliographic record
Abstract
Shear stresses on the running surface are believed to crucially determine the flow of snow avalanches. Measurements of shear and normal stresses on the running surface are presented as well as measurements of flow depth of snow flows down the Weissfluhjoch Snow Chute before and after a reduction of the chute’s inclination. In the measurements before the inclination change, maxima of measured normal stresses agreed with the maxima of the normal component of the column weight calculated using pre-release snow density. After the reduction of inclination, stresses increased considerably and the magnitude of the increase depended on the density of the flow. Using the measurements of normal stress and flow depth before the inclination change, a depth-averaged flow density was computed. The flow density was lower in the front and the tail of the avalanches and approached the pre-release density in the avalanche body. The ratio of measured shear to normal stresses, the coefficient of friction, was higher in wet snow flows than in dry snow flows. Upon analysis of the dependence of the coefficient of friction on parameters varying between the experiments, higher coefficients of friction for higher densities, snow and air temperatures, and average avalanche velocities were found. The total avalanche volume correlated negatively with the coefficient of friction. Measured coefficients of friction were generally lower as expected for flows of constant velocity, which might indicate the importance of other frictional processes such as friction at the snow–air interface, which is supported by the evolution of small dilute snow clouds on top of the flows that consisted of dry snow.
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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 | 0.001 |
| 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".