Local Calibration of the MEPDG Rutting Models for Ontario’s Flexible Roads: Recent Findings
Bibliographic record
Abstract
This paper summarizes the recent efforts for and major findings from local calibration of the rutting models of the AASHTO Mechanistic-Empirical Pavement Design Guide (MEPDG) for Ontario’s practices in pavement design, construction and maintenance. Unlike many other local calibration studies for rutting models, this study took a new calibration method built upon the more recent rutting calibration results from NCHRP Project 9-30A. To reduce the indeterminacy because of the unknown layer contributions of total rutting, two of the five local calibration factors (the temperature and traffic exponents) were prefixed based upon statistical analysis of the data obtained from Project 9-30A. The remaining three scale factors were determined by using a two-objective optimization strategy that eliminates bias and reduces residual errors. It was concluded that although the Superpave and Marshall mixes share the same set of traffic and temperature exponents, the scale factors are very different. A set of local calibration factors were recommended for future flexible pavement design in Ontario.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".