Pavement performance measures for Ontario provincial highways
Bibliographic record
Abstract
The current pavement performance measure used by the Ministry of Transportation Ontario (MTO is Optimal State of Repair (OSR), which relates to the percentage of provincial highways in good condition. OSR is calculated using the composite Pavement Condition Index (PCI), which combines the Ride Condition Index (RCI), based on International Roughness Index (IRI) measurements, and the Distress Manifestation Index (DMI). As part of the development and implementation of an Asset Management System, a comprehensive review of pavement performance measures was undertaken. The Ministry set out to determine what pavement performance measure(s) would best communicate the condition of the highway network and secure sufficient funding. A survey was carried out of performance measures used by other jurisdictions across North America. It found that many agencies were using IRI, especially in the USA where the Federal Highway Administration (FHWA) requires states to report road roughness on the IRI scale for inclusion in their Highway Performance Monitoring System (HPMS). However, results of the survey found that IRI does not change sufficiently on an annual basis to trigger allocation of funds. A composite index, similar to the PCI used in the Ministry's current performance measure was preferable.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.012 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".