Bibliographic record
Abstract
Measuring pavement performance is a major component of the pavement management system. It assists in decision-making for finding the optimum strategies to provide, evaluate, and maintain serviceability in an acceptable condition cost effectively. The Ontario Ministry of Transportation (MTO) has been systematically rating pavement performance since the mid-1960s. Pavement condition survey involves measurement of two physical parameters: ride quality of pavement surfaces, and the extent and severity of pavement distress manifestations. The pavement ride quality can be measured with an acceptable level of consistency and repeatability through automation. However, achieving consistency in the evaluation of pavement distress manifestations is a challenging task because the automation that could accurately and consistently detect, quantify and record surface distresses is not fully developed is spite of rapid advances in video imagery and non-contact sensing devices. This report evaluates the progress made over the past three decades in the key areas of Distress Manifestation Index, Riding Comfort Rating, Pavement Condition Index and second generation Pavement Management System (PMS2). A review of the Ministryʼs network-level pavement performance database is presented, emphasizing pavement condition surveys, prediction models and main factors influencing assessment of long-term pavement performance. Several key issues related to the quality control and quality assurance of the pavement roughness are discussed with reference to the verification techniques used by the MTO. Based on the literature review, future recommendations for possible improvements of the prediction models and techniques used for the evaluation of pavement performance are presented in order to obtain more consistent values.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".