Transportation Association of Canada
Bibliographic record
Abstract
This paper discusses the long-term pavement performance observed on Ontario’s low-volume roads. These low-volume roads, which carry fewer than 1,000 vehicles per day, comprise some 3,715 center-line kilometers in length, about 20 per cent of the total Ontario provincial road network. The long-term monitoring of pavement performance trends on these low-volume roads spans twenty years, and includes performance measures of pavement roughness, distress and overall pavement condition. Most of the observed pavement sections have been rehabilitated or re-constructed several times since 1985. The main objectives of this paper are to: 1) review the pavement rehabilitation and maintenance treatments applied on Ontario provincial highways over the last twenty years, focusing on observed pavement performance records of individual treatments versus age, construction costs and predicted performance curves, 2) analyze pavement life-cycle costs and overall long-term performance of the typical pavement structures used in the past, and 3) compare the pavement performance curves of specific pavement maintenance and rehabilitation (M&R) treatments applied to these low-volume roads. The paper starts with an introduction to the pavement rehabilitation and re-construction activities that are commonly used for low-volume roads in Ontario, which are listed in the Ministry’s pavement management system (PMS/2). It then discusses typical pavement M&R treatments, historical performance records and predicted performance trends, addressing the best practices in rehabilitating low-volume roads in Ontario. Finally, some preliminary findings and conclusions based on the long-term pavement performance observations and economic analyses 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.552 | 0.403 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".