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Record W3019178503 · doi:10.1139/cjce-2019-0574

Effect of particle size and CO<sub>2</sub> treatment of waste cement powder on properties of cement paste

2020· article· en· W3019178503 on OpenAlexvenueno aff
Hamideh Mehdizadeh, Tung‐Chai Ling, Xiongfei Cheng, Kim Hung Mo

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2020
Typearticle
Languageen
FieldEngineering
TopicConcrete and Cement Materials Research
Canadian institutionsnot available
Fundersnot available
KeywordsCementCarbonationMicrostructureMaterials scienceThermogravimetric analysisCalciteParticle sizePorosityCompressive strengthComposite materialParticle-size distributionMineralogyChemical engineeringChemistry

Abstract

fetched live from OpenAlex

This paper studies the role of CO 2 treatment and the impact of particle size (<75 μm and 75–150 μm) of waste cement powder (WCP) with different cement replacement content (0%, 5%, 10%, 15%, 20%, and 30%) on the physical properties and microstructure of blended cement paste. The results show that carbonation of WCP can effectively increase the flowability of paste due to the formation of calcite and decrease the porosity of WCP microstructure, while the water demand to achieve the same workability decreases with increasing size of WCP particles. Cement paste containing decreased particles of carbonated waste cement powder possesses a higher 28 day compressive strength due to formation of a higher amount of calcite and hydration products, based on the thermogravimetric analysis.

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)0.001
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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.014
GPT teacher head0.197
Teacher spread0.183 · 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

Citations108
Published2020
Admission routes1
Has abstractyes

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