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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 OpenAlex
Hamideh Mehdizadeh, Tung‐Chai Ling, Xiongfei Cheng, Kim Hung Mo

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

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 (&lt;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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.043
Threshold uncertainty score0.494

Codex and Gemma teacher scores by category

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)0.0000.000

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