Quantification of Influence Factors in the Studded Tire Wear Using the Prall Device
Bibliographic record
Abstract
Studded tire wear is widely considered to be a critical aspect that conditions the ride quality in cold regions. This research provides a comparative analysis of the influence of certain factors on pavement wear caused by studded tires. Abrasion resistance measurements were conducted on aggregates and asphalt specimens, using the Nordic Ball Mill apparatus and the Prall apparatus, respectively. This approach ensures an understanding of the interaction between these variables and the resultant wear response of the asphalt mixture. Tests relied on three different types of asphalt mixtures (ESG, dense-graded asphalt; EG, coarse-graded asphalt; and SMA, stone matrix asphalt), different percentages of reclaimed asphalt pavement (0%, 10%, and 20%), and different aggregate hardness. Image analysis methodology was used to assess the coarse-sectional area of the sample covered by coarse aggregates. The results showcased that harder coarse aggregates were less prone to wear. Comparisons indicate that the SMA10 yields higher abrasion resistance than the remaining mixtures used in this study. The percentage of the cross-sectional area covered by coarse aggregate influences the mixture abrasion value.
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".