Editorial: Selected papers from the 17th International Conference on Alkali–Aggregate Reaction (ICAAR) Ottawa, Canada, 2024
Bibliographic record
Abstract
We are pleased to present this special selection of papers from the 17th International Conference on Alkali–Aggregate Reaction (ICAAR), held in Ottawa, Canada, in 2024. The conference brought together a global community of researchers, engineers and practitioners to share the latest developments in understanding and mitigating alkali–aggregate reaction (AAR) in concrete infrastructure.While ICAAR 2024 covered a broad range of topics – from materials science to field diagnostics and sustainability – the collection in this issue highlights papers with a particular focus on modelling and structural aspects of AAR. These contributions were selected from among the many high-quality presentations at the conference, and they reflect the growing importance of predictive tools and structural performance assessments in managing infrastructure affected by AAR.The selected papers explore:These works highlight the critical role of modelling in bridging the gap between laboratory research and field application, enabling more accurate forecasting, risk assessment and decision making for infrastructure management.We thank all the contributors to ICAAR 2024 and hope this collection will serve as a valuable reference for advancing the structural and modelling dimensions of AAR research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.036 | 0.028 |
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".