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Record W2155865122 · doi:10.1680/macr.12.00015

Effect of nano-calcium carbonate on early-age properties of ultra-high-performance concrete

2013· article· en· W2155865122 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueMagazine of Concrete Research · 2013
Typearticle
Languageen
FieldEngineering
TopicConcrete and Cement Materials Research
Canadian institutionsWestern University
Fundersnot available
KeywordsCementMaterials scienceChlorideCorrosionNano-NucleationCalcium carbonateMetallurgyCarbonateComposite materialChemistry

Abstract

fetched live from OpenAlex

In this study, the effects of nano-calcium carbonate (CaCO 3 ) addition on the early-age properties of ultra-high-performance concrete cured at simulated cold and normal field conditions were investigated. The nano-CaCO 3 was added at rates of 0, 2·5, 5, 10 and 15% as a partial volume replacement for cement. Similar mixtures incorporating chloride- and non-chloride-based accelerating admixtures were also tested for comparison. Results indicate the high potential of nano-CaCO 3 to accelerate the setting and hardening process of ultra-high-performance concrete through providing nucleation sites, increasing contact points and increasing the effective water-to-cement ratio. Although nano-CaCO 3 exhibited a comparable or slightly lower accelerating effect to that of the chloride- and non-chloride-based accelerating admixtures, it brings a number of benefits to concrete production. These include the development of low-maintenance structures through eliminating the risk of steel corrosion induced by chloride-based accelerating admixtures, as well as a more environmentally friendly concrete through reducing the cement factor of ultra-high-performance concrete.

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)0.002
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 score0.019
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.029
GPT teacher head0.277
Teacher spread0.248 · 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