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Record W2777866713 · doi:10.7764/rdlc.16.3.447

Microstructural evolution of cement pastes blended with two types of volcanic materials

2017· article· es· W2777866713 on OpenAlexaff
Diana M. Burgos, Luisa M. Cardona, Álvaro Guzmán, Khandaker M. Anwar Hossain, Silvio Delvasto

Bibliographic record

Venuenot available
Typearticle
Languagees
FieldEarth and Planetary Sciences
TopicGeological and Tectonic Studies in Latin America
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsMaterials scienceCementThermogravimetric analysisPortland cementPozzolanCalcium silicate hydrateEttringitePozzolanic reactionCuring (chemistry)PozzolanaFourier transform infrared spectroscopyScanning electron microscopeComposite materialMineralogyChemical engineeringChemistry

Abstract

fetched live from OpenAlex

This article presents results of a research about the effect to incorporate Colombian volcanic materials as a replacement of ordinary Portland cement on its hydration process.For this particular case were used Tolima volcanic material (TVM) and Puracé volcanic material (PVM).Pastes with 100% ordinary Portland cement and blended cement pastes containing 20% by of weight volcanic materials were prepared.The monitoring and identification of the hydration characteristics were carried out using Fourier-transform infrared spectroscopy (FTIR), thermogravimetric analysis (TG/DTG) and scanning electron microscopy (SEM) at 7, 28, 90 and 360 curing days.The results indicated that the main phases of the blended cement pastes containing PVM were ettringite and calcium silicate hydrate (CSH), which confirmed the pozzolanic activity of this material.The blended cement pastes containing TVM exhibited no significant differences in hydration products compared to the reference pastes, due to its inert nature.

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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.012
GPT teacher head0.234
Teacher spread0.222 · 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

Citations1
Published2017
Admission routes1
Has abstractyes

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