Bibliographic record
Abstract
The new Mechanistic Empirical Pavement Design Guide (MEPDG) has been developed based on fundamental properties of materials and the physical observations of performance. It can be used for all truck volume and axle load scenarios. However, for a more reliable design, local material properties, climate data, truck volume and distributions, and axle load spectra (ALS) are critical. This paper presents the experience of Manitoba Infrastructure and Transportation (MIT) with the MEPDG in using the local truck traffic data with an example of a flexible pavement design. The sensitivity of the program for changes in truck volume, ALS and truck distributions are presented. Analysis/experience showed that MEPDG produces designs with similar or thinner pavement structures for low truck volume but it overestimates the pavement structures for moderate to high truck volumes compared to the AASHTO 1993 and surface deflection methods. A significant variation in required structure was also noted for a within province variation in the truck class distribution. This emphasizes the importance of calibrating the performance models to local conditions. The issues and challenges in calibrating the MEPDG performance models are also discussed. For the covering abstract of this conference see record control number 201111RT334E.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".