Survey on Current Practices for Evaluating Warm Mix Asphalt Moisture Susceptibility
Bibliographic record
Abstract
Warm Mix Asphalt (WMA) technology is becoming more commonly used by transportation agencies. This is due to the pressures from environmental acts and agencies to reduce Green House Gas (GHG) emissions generated by production and placement of paving mixtures. However, WMA technology has certain functional considerations, namely moisture susceptibility that need to be addressed in order to achieve performance equivalent to conventional Hot-Mix Asphalt (HMA) with the additional benefits of reduce emissions. To identify possible gaps in the WMA that need to be closed with further research, a Canada-wide survey has been prepared at the Centre for Pavement and Transportation Technology (CPATT) located at the University of Waterloo to document the state-of-the-art related to WMA technologies. This paper presents the results of this survey on the preferred practices conducted by various Canadian transportation agencies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".