Improving Canadian Performance of SMA by Maximizing Best Practices
Bibliographic record
Abstract
Stone Mastic Asphalt (SMA) is a durable, rut resistant mix that relies on a stone-on-stone contact to provide strength while utilizing a rich mortar binder to provide durability. It has been used in Canada since 1990 and in the United States since 1991. Although it provides many technical and economic benefits, SMA requires detailed attention during the production and placement process. The stone-on-stone contact which provides the durability of the mix can be achieved through proper gradation and the use of hard durable aggregate. In addition, the mix must be properly designed in terms of the asphalt content, the associated air void content, and the appropriate design for voids in the mineral aggregate must be selected. Finally, the mix must meet moisture susceptibility and draindown requirements. In order to achieve a good product in the field, special attention must be given to ensure good production and construction practices. At the 2004 Canadian Technical Asphalt Association Contractor's Workshop, which focused on SMA pavements, it was evident that contractors, consultants, and transportation agencies are struggling to achieve quality with these pavements. Various problems had been identified ranging from mix design issues related to the use of fibers, to plant production problems associated with the addition of fines, to compaction issues, and draindown in the field. This paper is thus directed at providing state of the art practice in SMA pavements in the Canadian context.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".