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Record W1919114240 · doi:10.1520/stp157720130134

Feasibility Study on Replacing Steam by Carbon Dioxide for Concrete Masonry Units Curing

2014· book-chapter· en· W1919114240 on OpenAlexaff
Yixin Shao, Vahid Rostami, Yaodong Jia, Liang Hu

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldEngineering
TopicConcrete and Cement Materials Research
Canadian institutionsMcGill University
Fundersnot available
KeywordsMasonryCuring (chemistry)Materials scienceCarbon dioxideWaste managementComposite materialEnvironmental scienceStructural engineeringEngineeringChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

The feasibility of replacing steam by carbon dioxide in concrete masonry units (CMU) curing was studied for carbon dioxide utilization and performance improvement. A laboratory study had shown that CMU could uptake 18 %–24 % CO2 by mass based on cement content through a carbonation period of 2–4 h. In comparison to steam process, carbonated CMUs could have equivalent strength and much improved durability performance. If all CMUs produced in the United States can be treated by carbon dioxide curing, approximately 1.5 × 106 tonnes of CO2 will be utilized each year. If this CO2 is captured from cement kilns, the reduction of carbon emission for US cement industry will reach 3 % due to the contribution by CMU industry alone. The carbon dioxide curing of CMU will facilitate carbon capture and storage with benefits to both environment and business.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.045
GPT teacher head0.271
Teacher spread0.226 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2014
Admission routes1
Has abstractyes

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