Effectiveness of dominant aggregate size range – interstitial component criteria for consistently enhanced cracking performance of asphalt mixtures in the field
Bibliographic record
Abstract
This study primarily focused on evaluating the effectiveness of dominant aggregate size range – interstitial component (DASR-IC) criteria established for the purpose of asphalt mixture design and construction specifications leading to better and consistently enhanced field cracking performance using the enhanced hot-mix asphalt fracture mechanics-based performance prediction model (HMA-FM-E model) plus field performance evaluation. Results indicated that the mixtures meeting all DASR-IC criteria, including DASR porosity, disruption factor (DF), effective film thickness (EFT), and fine aggregate ratio (FAR), will have relatively better and consistently enhanced field cracking performance. The mixtures not meeting all DASR-IC criteria show inconsistent field cracking performance, including either cracked or uncracked status. Thereby, it is important to design asphalt mixtures that meet all DASR-IC criteria. The DASR-IC criteria were found to be effective and their implementation will help ensure consistently enhanced cracking performance in the field.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".