Evaluation of the impact of recycled glass on asphalt mixture performances
Bibliographic record
Abstract
The goal of this research was to verify the possibility of using recycled glass particles in an asphalt mixture while maintaining equivalent properties and performance in lieu of a conventional mixture. First, one type of asphalt mixture (ESG14) with different glass contents was tested according to the Ministère des transports du Québec’s mix design method. Next, the performances (resistance to thermal cracking, mixture stiffness and stripping resistance) of an asphalt mixture with optimal glass content were evaluated and compared to a reference mixture. Overall, it was found that using recycled glass in an ESG14 asphalt mixture reduces the binder content, increases the mixture workability and decreases the rutting resistance. It was also found that using 10% recycled glass in an ESG14 asphalt mixture does not impact the resistance to thermal cracking as well as the mixture stiffness. On the other hand, the stripping resistance is negatively affected by the presence of glass.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".