TAC Pavement ME User Group - Canadian Climate Trials
Bibliographic record
Abstract
The major objective of the NCHRP 1-37A project was to develop a document for adaptation by AASHTO as its new Mechanistic-Empirical Pavement Design Guide (MEPDG) for new and rehabilitated pavement structures. The development of the new AASHTO MEPDG and its supporting software DARWin ME (later re-named to Pavement ME Design), has changed existing pavement design procedures, but would need to be adapted and calibrated for local conditions. The Pavements and Soils and Materials Standing Committees of the Transportation Association of Canada initiated a Pool Fund project to provide guidance to Canadian agencies on adaptation and calibration of the MEPDG to Canadian conditions. The calibration process began with the development of a database of historical Canadian climate information for various locations across the country, which has since been included in Pavement ME Design. The user group began running trial designs and comparing the variability in results. A flexible pavement design was provided by Manitoba Infrastructure and Transportation that was used as the base design. This base design was modified for various climate stations across Canada, while keeping all other input parameters consistent. Design trials were then completed by changing the season in which the pavement structure was constructed, followed by evaluating the impact of changing asphalt cement grade in the asphalt mixtures. The objective of this paper is to present the results of the trials completed by the TAC Pavement ME user group, and highlight some of the findings of the analysis. For the covering abstract of this conference see ITRD record number 201310RT334E.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 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) | 0.022 | 0.004 |
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".