Database Development for Ontario's Local Calibration of Mechanistic-Empirical Pavement Design Guide (MEPDG) Distress Models
Bibliographic record
Abstract
The AASHTO Mechanistic-Empirical Pavement Design Guide (MEPDG) includes empirical distress models that need both global and local calibrations. The local calibration requires developing a database that would reflect local environments, design and maintenance practices in a particular jurisdictional region. The objective of the thesis is to develop a pavement database for local calibration before the MEPDG is to be implemented in Ontario. The database involves a hierarchical framework of the input parameters required for DARWin-ME, and the measured performance data are based on the MTO’s PMS-2. To demonstrate the validity of the developed database a preliminary local calibration including clustering analysis is carried out for the IRI and total rutting. The calibration-validation analysis suggests that the IRI model can be best clustered based on the geographical zone whereas the highway functional class is the best clustering parameter for rutting during the local calibration.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.009 |
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".