Benchmarking of Compression Testing Devices in Snow
Bibliographic record
Abstract
Traction testing of tires for 'severe snow use' certification is performed according to ASTM F1805 in the Unites States and Canada. The ASTM standard provides guidelines for the preparation and compaction of the snow required for the testing and its evaluation using the CTI (Compliance Testing Incorporated) penetrometer. However, the CTI penetrometer provides a measure of the degree of compactness of the snow. From a computational perspective, this information in itself is not sufficient for modeling and/or validation of a snow model for virtual snow traction testing, which is important in the design stage of tires. This work attempts to compare the relative compression testing performance of three devices, namely a CTI penetrometer (rounded cone tip impactor), a standard Clegg Hammer (flat surface impactor), and an in-house developed device inspired by the Russian Snow Penetrometer (cone tip impactor). The approach used here includes a comparison of the available direct and indirect outputs of all the devices and a statistical analysis of these. The observed differences may be due to differences in the mass and drop height between the devices. The findings of this work provide a baseline for further research into the optimal mass and drop height for the evaluation of compacted snow properties. From a modeling perspective, the values generated using the Clegg impact hammer could be useful. However, it tends to underestimate the elastic modulus, when its results are compared to the laboratory technique, and to overestimate the ram resistance when compared to the in-house device. Further improvements to the in-house device may improve its ability to measure the snow properties most relevant to the modeling of 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".