Engineering Tools and Standards Applied in Preserving Ontario’s Provincial Highways
Bibliographic record
Abstract
This paper presents the road asset management concepts, engineering standards and technical tools developed by Ministry of Transportation of Ontario (MTO) for preservation of Ontario provincial highways in the areas of Corridor Investment Plans (CIP). The paper starts with an introduction to the Asset Management Business Framework (AMBF) that highlights some of the key system components and investment analysis tools applied in asset management. The paper then presents how MTO’s Second Generation Pavement Management System (PMS2) fits into the ongoing Asset Management (AM) activities in terms of corridor investment plans (CIP) and multi-year rehabilitation and maintenance program, with emphases on pavement performance measures, commonly used pavement rehabilitation treatments in Ontario, observed pavement performance and prediction models, integration of preventative maintenance strategies with rehabilitation treatments. An example application of a corridor investment plan is used to illustrate the key characteristics and functional capacities of the newly enhanced PMS2, including road network database management, performance evaluation and prediction, and customized decision trees based upon forecasted road conditions and targeted service levels. The
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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 | 0.011 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".