Sustainability Metrics of Two Pavement Rating Systems Developed in Canada
Bibliographic record
Abstract
Sustainability is an increasingly important consideration in road building across North America. Pavement sustainability can be evaluated using several different methods or tools, including life cycle assessment, life cycle cost analysis, performance assessment, and sustainability rating systems (SRS). A SRS is basically a list of sustainability best practices with a related measure, usually a point score, which quantifies each best practice in a common unit. In an effort to bring awareness of and promote “green” initiatives to designers, both the Ontario Ministry of Transportation (MTO) and Golder Associates created user-friendly SRS to promote sustainable pavement technologies for the design, construction, rehabilitation, reconstruction, and preservation of roads. The MTO system is known as GreenPave and the Golder system as GoldSET. This paper describes the development and implementation of these two SRS and compares their analysis through two case studies, one dealing with a rigid pavement rehabilitation project and the other addresses a flexible pavement project.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".