Validation and Implementation of Ontario, Canada, Network-Level Distress Guidelines and Condition Rating
Bibliographic record
Abstract
The Centre for Pavement and Transportation Technology at the University of Waterloo and the Ministry of Transportation of Ontario (MTO) have been studying for the past 4 years the suitability of applying automated technologies for network-level evaluations in the province. Three projects have been developed for this purpose. The main results of these studies were a better understanding of available digital technologies, evaluation of the performance of semiautomated and automated technologies, development of new guidelines for pavement distress collection at the network level, design of an adjusted distress manifestation index, and recommendations for the use of semiautomated and automated digital technologies at the network level. The objective of this paper is to present the findings of the third and last phase of the project, Validation and Implementation of MTO Network Level Automated–Semiautomated Pavement Distress Guidelines and Condition Rating Methodology. The scope of the study was to validate and implement in the field MTO network-level distress guidelines and a distress manifestation index for network-level evaluations (DMI NL ), considering the use of automated technologies. A complete statistical analysis of data collected in the field through manual evaluations and semiautomated and automated technologies is presented. The performance of currently available technologies using network-level distress guidelines was assessed. Finally, from the field validation, distress guidelines were adjusted accordingly and DMI NL equations were recalibrated.
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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 | 0.017 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".