Rolling Resistance and Wet/Snow Traction Performance of Commercially Available Light-Duty Vehicle Tires in North America (Canada) Pt.II
Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">Low rolling resistance tires are a technology used to improve fuel economy and reduce greenhouse gas emissions in the transportation sector. This project analyzed current relationships between environmental and safety performance properties of commercially available light-duty tire models in Canada. This paper presents the results of a blinded multi-year light-duty vehicle tire research project conducted by Transport Canada &amp; Natural Resources Canada. The study follows on an update to SAE WCX 2018-01-1336 which presented results for tires tested between MY2014-2018. Tire performance was evaluated in a variety of tire categories with a focus on wet grip and rolling resistance. Correlations between key performance indicators were charted to analyze trends in new model tires available on the Canadian tire market. Manufacturer specifications were also charted to evaluate the relations of wet grip and rolling resistance with price, UTQG ratings, and marketing categories. Testing for rolling resistance was done using the single point rolling resistance coefficient method via ISO 28580. Wet grip index values were measured as a metric of performance through UNECE Reg.117, with snow and ice performance ratings from ASTM 1805. The data presented in the following paper provides empirical insight into the light-truck, “new generation” winter tire market, original equipment manufacturer tire (OEM), and an assorted winter tire testing campaign. Important context was added in these markets, to provide regulators with information that can be used to assess a potential minimum energy performance standard (MEPS) for replacement passenger tires in support of policy goals. 155 unique model tires were tested within the 2018-2022 campaign further adding to the 138 presented in the previous edition of this paper (SAE WCX 2018-01-1336).</div></div>
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".