Quantifying the Sustainable Benefits of Preserving Canada's Road Assets
Bibliographic record
Abstract
The Ministry of Transportation of Ontario (MTO) has implemented numerous innovative pavement preservation strategies in recent years to maximize cost savings in repair operations and extend pavement life in a sustainable manner. These strategies are considered sustainable as they improve pavement quality and durability, and extend pavement service life, while reducing energy consumption and green house gas (GHG) emissions. This paper outlines the various flexible pavement preservation treatments utilized by MTO to achieve sustainability including micro-surfacing, chip seal, ultra-thin bonded friction course, fiber modified chip seal, and hot in-place recycling (HIR). Pavement preservation sustainability is quantified in a case study using the PaLATE software by comparing the energy consumption and GHG emissions generated for various flexible pavement preservation strategies, against a conventional rehabilitation treatment on a life cycle basis. The results indicate that these innovative pavement preservation strategies significantly reduce energy use and GHG emissions when compared to traditional treatments. The paper quantifies the sustainable benefits of innovative flexible pavement preservation treatments in terms of energy and material savings and GHG emission reductions. An innovative environmental rating system to promote sustainable preservation of road assets is also discuss
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".