Editorial: Special Section on Infrastructure and Bituminous Materials
Bibliographic record
Abstract
This special section on infrastructure and bituminous materials of ASTM International’s Journal of Testing and Evaluation includes 20 peer-reviewed papers. These papers are extended versions of selected best papers presented during the RILEM International Symposium on Bituminous Materials (ISBM Lyon 2020), which took place on December 14–16, 2020. This event was organized by ENTPE at the University of Lyon (France), in collaboration with the University of Parma (Italy), Université Gustave Eiffel (France), and University of Waterloo (Canada). RILEM ISBM Lyon 2020 was the first joint event of three RILEM Technical Committees of Cluster F (“Bituminous Materials and Polymers”): 264-RAP (“Asphalt Pavement Recycling”)272-PIM (“Phase and Interphase Behaviour of Bituminous Materials”)278-CHA (“Crack-Healing of Asphalt Pavement Materials”)The articles included in this special section cover a wide range of topics, offering an interesting overview of the latest advancements in the domain of sustainable infrastructures: crumb rubber, aging of materials, hot and cold recycling, low-energy materials, damage, fatigue, cracking and self-healing, fume emissions, rheology of materials, low-temperature properties, non-destructive testing, polymer-modified materials, and in situ testing. Such a variety of topics is a small but significant sample of the richness of the scientific and technical activities within the domain of infrastructures and pavement engineering, from fundamental to applied research works, contributing to the development of innovative materials and technologies. RILEM plays an important role, fostering international collaborations between academia and industry, nourishing the essential network between scientific and technical activities. Last but not least, we would like to extend our warm thanks to all the reviewers that kindly agreed to evaluate all the articles and contributed to the scientific quality of this special issue.
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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 | 0.004 | 0.003 |
| 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 | 0.001 |
| 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".