Evaluation of Warm-Mix Asphalt Produced with the Double Barrel Green Process
Bibliographic record
Abstract
During the past 3 years, warm-mix asphalt (WMA) technologies from European countries have entered the North American market. European experiences with WMA technologies have indicated that a significant reduction in mixture temperature, mixture viscosity, energy consumption, and environmental emissions during asphalt mix production and placement can be achieved in comparison with traditional hot-mix asphalt (HMA). On the basis of North American experiences with these technologies to date, transportation agencies and HMA producers are unlikely to adopt WMA technologies solely for the reduction in manufacturing energy costs and environmental emissions, because these benefits do not cover the associated increase in investment and additive costs of WMA over HMA, even in the most expensive North American energy markets. This paper presents an evaluation of the economic, environmental, and mixture performance factors to assess the sustainability of WMA in North America. The paper examines the benefits, risks, investment and material costs, and sustainability associated with the different WMA technologies and specifically the Double Barrel Green process. Included is a mixture performance evaluation of WMA mixes containing reclaimed asphalt pavement and Manufactured Shingle Modifier produced with the Double Barrel Green System during field trials in Vancouver, British Columbia, Canada.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".