How To Predict Truck Tyre Wear
Bibliographic record
Abstract
Durability and end of life considerations of products are important, especially regarding microplastic pollution . Tyres significantly contribute to this problem, with nearly 30% of the microplastics originat ing from tyre wear. Therefore predicting tyre wear and improving abrasion resistance of tyres are essential to decrease the microplastics. <br/>Tyre wear is a complicated phenomenon, dependent on multiple factors , and predicting it for truck tyres remains a challenge. This work presents research on rubber abrasion, to understand factors influencing truck tyre abrasion and current laboratory measurement methods. Multiple factors were investigated and correlations between results from LAT100 analysis and material characteristics were evaluated. Results show linear abrasion dependence on the load and a positive correlation between shear strength and a brasion.<br/>The abrasion was tested using both the DIN abrader and LAT100, with the latter<br/>showing better correlation between lab and road data. However, current LAT100<br/>protocols for car tyres are suboptimal for truck tyres. It requires further improvements like increasing the energy dissipation , reduced speed, increased load and disc sharpness. <br/>Future work will optimise the LAT100 protocol for testing truck tyre compounds<br/>and additional compounds will be developed to better understand wear behaviour and material properties.
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How this classification was reachedexpand
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 | 0.000 |
| 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 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".