Balancing the Use of Wax-Based Warm Mix Additives for Improved Asphalt Compaction with Long-Term Pavement Performance
Bibliographic record
Abstract
Using wax-based warm mix additives allows contractors to lower production and compaction temperatures of asphalt, thereby reducing greenhouse and other harmful gas emissions in pavement construction. However, excessive wax can adversely affect the long-term durability of the pavement. In order to quantify solid wax, the effects of selected commercial additives on spectral and thermal properties of asphalt binder were studied by variable-temperature Fourier-transform infrared spectroscopy (VT-FTIR) and differential scanning calorimetry (DSC). The VT-FTIR reduced spectral area versus temperature plots for wax-doped asphalt binder were found to have three distinct parts from which solid wax contents could be determined. The wax precipitation temperature (WPT), obtained from DSC measurements of heat flow during cooling, was found to increase with additive content. In contrast, the wax melting out temperature (WMT), determined upon heating, appears to be independent of the additive content.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".