Manufactured aggregate from cement kiln dust
Bibliographic record
Abstract
This paper presents the results of a laboratory study that evaluated the geotechnical and geoenvironmental properties of a manufactured aggregate derived from cement kiln dust (CKD). The aggregate manufacturing process involves accelerated carbonation technology (ACT), has been used to treat contaminated soils at trial scale. The process operates at commercial scale in the UK, producing aggregates from thermal residues. The ACT process relies on the accelerated reaction of carbon dioxide with the calcium oxide in the CKD material in the presence of water. No additional binder was used in this study, relying solely instead on the formation of carbonate to form the aggregate. In this paper, the aggregate manufacturing process is briefly described. To explore future potential construction applications of the aggregate, several geotechnical test results are used to assess strength and durability (i.e. individual particle strength, internal shear strength of the particle assemblage, wet–dry testing, freeze–thaw testing). Screening tests on the aggregate’s geoenvironmental characteristics are discussed (metal leaching, dissolved heavy metal adsorption and hydraulic conductivity) to assess potential uses further. It is shown that the aggregate studied has adequate properties for a variety of construction applications, but is unsuitable for use in freezing and thawing environments.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".