Successful Implementation of Warm Mix Asphalt in Ontario
Bibliographic record
Abstract
Warm Mix Asphalt (WMA) has proven to be an innovative green technology that improves the environmental sustainability of Hot Mix Asphalt (HMA) by reducing emissions and conserving energy while maintaining or enhancing pavement performance through improved compaction. Approximately 500,000 tonnes of WMA have been paved on Ministry of Transportation of Ontario (MTO) roadways since 2008. WMA technologies that have been used include both chemical and organic additives. While WMA has many benefits, it also has challenges that are being addressed by a joint task group comprised of members from MTO and the asphalt paving industry. In 2010, MTO built several WMA test sections along with HMA control sections to evaluate the performance and environmental benefits of WMA. Emission measurements were conducted at the asphalt plants as well as at the paving sites for both WMA and HMA mixes. Laboratory investigations included moisture sensitivity testing on production samples, Hamburg wheel track testing, coating, compactability, and Flow Number. MTO is monitoring the performance of the WMA pavement sections based on the distress data obtained by MTO’s Automated Road Analyzer (ARAN). Pavement performance of WMA has been comparable to HMA, with slightly better joint quality. Given the positive experience with WMA, MTO adopted a permissive specification in 2012 allowing the contractors to use WMA in lieu of HMA. In conjunction with this specification, the desire to grow the WMA market has prompted MTO to continue to build WMA projects in 2013 and 2014, by specifying its use. This paper presents quality assurance and emissions data collected during construction of our WMA projects. The paper also discusses laboratory test results
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".