Opening Letter of RILEM TC UMW: Upcycling Powder Mineral Wastes into Cement Matrices — Challenges and Opportunities
Bibliographic record
Abstract
The cement and concrete industries are currently facing the urgent and arduous challenge of decarbonisation and material circularisation for improved resource efficiency. The pursuit of new raw materials and binders that will improve sustainability is urgent, especially as end-of-pipe carbon capture and storage (CCS) technologies have not yet been scaled up economically even after five decades of research and large investments. On the other hand, society is facing the colossal issue of managing mineral wastes which are produced in several Gts per year globally, posing a massive environmental and societal liability. Many of these mineral wastes have elemental and mineralogical profiles that make them good candidates for use as clinker raw feed or supplementary cementitious materials. Although the published research on the topic is extensive, it is not organised, lacking a systematic comprehensive approach, making valorisation challenging. RILEM TC UMW was developed to address this gap and create a framework for realising the potential of upcycling mineral wastes focusing on using powders as either clinker raw feed or other binder applications while excluding discussion on calcined clays and mineral carbonation.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.015 | 0.015 |
| Insufficient payload (model declined to judge) | 0.008 | 0.007 |
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".