Verification of Network-Level Pavement Roughness Measurements
Bibliographic record
Abstract
In 1997 the Ministry of Transportation of Ontario (MTO), Ontario, Canada, switched to international roughness index (IRI) measurements as an indicator of network-level pavement roughness within its pavement management system. Measurement of pavement roughness or ride quality in terms of IRI can be performed with different measuring devices. However, the individual measurements for the same pavement section may vary significantly because of the use of different measuring devices, different longitudinal profiles, and different measuring speeds. Recent evidence obtained by MTO indicates that the longitudinal profile measurements provided by different measuring devices contain systematic differences that range from 0.1 to 1.0 m/km. Such differences in IRIs cause significant concerns for agencies that contract out collection of network-level roughness measurements on a yearly basis. Through the process of verification and comparison of longitudinal profile measurements obtained with different profilers, MTO has gained insight into the functional relationships and factors that affect profile measurements in terms of precision and bias. The verification techniques used to obtain normalized, reproducible, and time-stable IRI measurements for IRIs supplied by different IRI providers are described. Preliminary findings and statistical analyses of IRI values measured on a verification circuit, which is composed of 12 sections with four different pavement types, are provided. In addition, the results of analyses of the various IRI measurements and their impacts on network-level pavement serviceability are discussed.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".