APPLICATION OF PAVEMENT MANAGEMENT TOOLS IN THE PROCESS OF CONSTRAINED REGIONAL INVESTMENT PLANS –
Bibliographic record
Abstract
This paper presents the ongoing development of Constrained Regional Investment Plans (CRIPs) over a period of 25 years for pavement preservation through application of the Ministry’s Second Generation Pavement Management System (MTO PMS2). This project was initiated by the Ministry of Transportation of Ontario (MTO) in March 2010 as part of the Ministry’s long-term highway network asset management. A series of investment and performance evaluation analysis scenarios are conducted through MTO PMS2 applications, including predictions of road network performance trends given budget constraints, and determining the funding needed to achieve long-term pavement performance targets for individual regional and provincial highway networks. The requirements for developing CRIPs and the existing pavement management capabilities are described by presenting an example of the application of the MTO PMS2 to planning for regional highway performance targets and funding levels at network level. The paper concludes with main findings and recommendations for rationalizing pavement performance measures and investments for individual regional road networks, and discussions on technical issues and challenges relating to pavement preservation strategies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".