Experimental analysis of snow micropenetrometer (SMP) cone penetration in homogeneous snow layers
Bibliographic record
Abstract
The cone penetration test (CPT) is widely used to determine in situ soil and snow characteristics and stratigraphy. For avalanche forecasting, knowledge about snow stratigraphy is of crucial importance. Portable electric cone penetrometers have therefore been developed with the goal of obtaining rapid and accurate measurements of snow stratigraphy. The most widely used electric penetrometer is the snow micropenetrometer (SMP), a constant-speed small-diameter cone penetrometer. The SMP was specifically designed to study snow and is not a penetrometer in the geotechnical sense, as the diameter of the SMP cone (5 mm) is comparable to the typical size of snow grains (0.1 to 1 mm). Previous numerical and experimental studies of the CPT in granular materials have highlighted the importance of material compaction around the cone. Nevertheless, given the high porosity of snow, compaction of failed elements around the SMP cone is generally neglected when interpreting SMP force signals. To verify this assumption, microcomputed tomography and particle image velocimetry were used to investigate the deformation of snow during SMP cone penetration. Results from laboratory experiments with uniform snow show that a compaction zone around the SMP tip develops during penetration. The size of the compaction zone was on average twice as large as the actual size of the cone, increasing with increasing snow density. Furthermore, an average penetration depth of about 40 mm was required for the compaction zone to develop fully. This critical penetration depth roughly decreased with increasing snow density. These results show that the compaction zone around the SMP tip is far from negligible and has to be accounted for when interpreting SMP force measurements.
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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 | 0.000 | 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 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".