Rehabilitation of York Region Road 40 Using a 6.7mm SMA and Other Innovative Hot and Cold Mix Products
Bibliographic record
Abstract
York Region Road 40 (Bloomington Road) is a two-lane roadway that carries more than 10,000 vehicles per day with approximately 10 percent heavy commercial vehicles mostly hauling aggregate to the Greater Toronto Area. Prior to rehabilitation, the pavement was severely oxidized and thermal cracking was extensive. The pavement design included an in-place recycling technique to mitigate reflective cracking and 100 mm of new surfacing HMA. The partial depth recycling process was selected using a rapid curing system to accelerate the build up of cohesion of the recycled material. A heavy duty dense graded Hot Mix Asphalt (HMA) was selected as a binder course, while and a 6.7 mm Stone Mastic Asphalt (SMA) was selected as a thin surfacing course. Both HMA mixes were tested for rutting using a rut testing device to ensure rut resistance performance. This paper presents the Bloomington Road project including details related to the roadway characteristics, the pavement design, the material and process selection, and the field construction. Finally, performance information of the various techniques is provided with an emphasis on the surface characteristics of the 6.7 mm SMA, including skid resistance and noise reduction. For the covering abstract of this conference, see ITRD number E215112. (A)
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.010 | 0.002 |
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".