Production of Cleaner Hot Mix Asphalt (CHMA) in Canada
Bibliographic record
Abstract
The production of sustainable asphalt mixtures is a challenge for the asphalt industry as a whole. However, new technologies such as Warm-Mix Asphalt (WMA) appear to produce more environmental friendly asphalt mixtures, helping meet this challenge of sustainability. There are still many obstacles in developing this technology, though, such as making the required modifications to asphalt mixing plants, cost of mix production and the long-term performance of WMA. Currently, WMA is produced and evaluated using the same mixture design and performance tests as Hot-Mix Asphalt (HMA), but it seems that the current mixture design methods should be improved by evaluating the Environmental Polluting Potentials (EPPs) of materials in the asphalt mixture. In this paper, the effects of aggregate sources were studied as regards the parameters proposed as indicators for EPPs of materials as correlates to the production of HMA in Canada, as a case study. The results clearly indicated that Cleaner Hot-Mix Asphalt (CHMA) can be produced irrespective of asphalt binder, aggregate and industrial fuel types in the mixing plants. The results also showed that the total amount of saved fuel, based on the EPP of materials, is equivalent to the amount required energy to energize anywhere from4000 to 14503 Canadian households per annum, indicating a significant amount of energy saving without any modification to the asphalt mixing plants. (A) 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".