Comparative Long-Term Performance of Canada’s First Stone-Mix Asphalt Freeway Project
Bibliographic record
Abstract
The first major high-volume freeway trial of Stone Mastic Asphalt (SMA) in Canada was constructed on Highway 401 west of Toronto in 1996. SMA is a heavy duty, gap-graded hot mix asphalt, composed of 100 percent crushed coarse aggregate and mastic stabilized asphalt cement. The performance to date has shown that the SMA aggregate skeleton with stone-on-stone contact will withstand rutting due to heavy truckloads. Additional asphalt cement binder provides increased durability, and resistance to aging and cracking of the mix. Stabilization of the additional asphalt cement and prevention of binder run-off during construction are achieved through an increase in fines and filler, the addition of fibres, and polymer-modification. This paper describes the 10-year performance of Canada’s first full-scale SMA freeway trial. SMA was constructed on Highway 401 adjacent to a Dense Friction Course (DFC) for the purpose of comparing field performance. Since construction in 1996, the Ministry of Transportation of Ontario (MTO) has been monitoring the performance of both the SMA and DFC mixes. Performance evaluation has included annual roughness and rutting measurement, frictional properties, and manual distress surveys. The results of ten years of performance evaluation are presented in detail, with the results indicating that both the SMA and DFC are performing well.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".