Bibliographic record
Abstract
The Ministry of Transportation of Ontario, Canada (MTO), is dedicated to maintaining quality roadways in a sustainable manner. In recent years, MTO has implemented pavement preservation strategies to maximize cost savings in repair operations and to maintain pavement condition. Pavement preservation treatments are considered sustainable because they improve pavement quality and durability and extend pavement service life, while reducing energy consumption and greenhouse gas (GHG) emissions. Pavement preservation is a proactive, planned strategy that extends the life of the pavement and provides a cost-effective solution for pavement management. This paper outlines the various pavement preservation treatments used by MTO to achieve sustainability. These preservation treatments include crack sealing, slurry seal, microsurfacing, chip seal, ultrathin bonded friction course, fiber-modified chip seal, hot-mix patching, and hot in-place recycling. With use of the PaLATE software, pavement sustainability is quantified by comparing the energy consumption and GHG emissions generated for various pavement preservation strategies against typical rehabilitation and reconstruction treatments. This paper presents the benefits of pavement preservation by considering the service life of each treatment and calculating the associated energy consumption and GHG emissions per service year. Results indicate that pavement preservation strategies provide a significant reduction in energy use and GHG emissions when compared with traditional rehabilitation and reconstruction treatments. Although pavement preservation has been proved to be a cost-effective solution, there are numerous challenges and barriers to overcome. Some of the challenges and solutions as well as the strategies to promote pavement preservation for sustainability are presented in the paper.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.037 | 0.010 |
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".