Revising Specifications to Enable New Materials and Adapting to Performance of Low-Clinker Concretes
Bibliographic record
Abstract
To support the concrete industry’s goals of meeting net-zero carbon-dioxide emissions by 2050, new types of lower-clinker cements are being developed for use. While Portland cement will continue to be used as part of the cementitious materials in the bulk of applications, use of Portland cement as the sole binder in concrete is diminishing globally. Reducing the clinker content of concretes is complicated by the limited and diminishing availability of traditionally used Supplementary Cementitious Materials (SCMs), such as fly ash and ground granulated blast furnace slags, whether added as separate additions to concrete or as components of blended cements. This has spawned increased interest and use of natural pozzolans such as volcanic materials, calcined clays, and other novel activated pozzolans. However, some of these activated or manufactured pozzolans either may not meet the current definition of pozzolans, and some may not meet some of the prescriptive limits in current specification. Performance specifications will help in the adoption of new SCMs and low-clinker cements that are being developed and coming to the market. The rapid changes in the availability of cementitious materials, combined with the need to accelerate the adoption and use of concrete mixtures provided they demonstrate equivalent performance to traditionally specified concretes, requires rapid adoption of performance specifications and test methods.
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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 | 0.023 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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".