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Record W3094804613 · doi:10.1080/1064119x.2020.1841347

Impact of salinity on <i>strength and microstructure of</i> cement-treated Champlain Sea clay

2020· article· en· W3094804613 on OpenAlexafffund
Moulay Youssef Monsif, Jinyuan Liu, Naresh Gurpersaud

Bibliographic record

VenueMarine Georesources and Geotechnology · 2020
Typearticle
Languageen
FieldEngineering
TopicConcrete and Cement Materials Research
Canadian institutionsToronto Metropolitan University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMicrostructureCementPorosityCompressive strengthMaterials scienceCementitiousComposite materialFlocculationScanning electron microscopeLeaching (pedology)Aggregate (composite)GeologyChemical engineering

Abstract

fetched live from OpenAlex

An experimental investigation was conducted to understand the impact of salinity on the microstructure, phase composition, and shear strength of cement-treated Champlain Sea clay. Based on a scanning electronic microscopy (SEM) analysis, Champlain Sea clay exhibits an open structure fabric with flocculated particles. Leaching results in de-flocculation of the aggregates and leads to an increase in inter-aggregate porosity in the clay. Cement mixing transforms a dispersed microstructure into a flocculated state with a large amount of clay-binder aggregates. Cementitious products were formed and reduced both inter-aggregate porosity and intra-aggregate porosity in the cement-treated clay specimens. Sodium chloride salt has a negative effect on the strength development of cement-treated Champlain Sea clay. Under the same cement dosage of 50 kg/m3, the unconfined compressive strength of cement-treated leached clay samples exhibited higher shear strength values than those at natural or a higher salinity level. An optimum salinity level was found to be at 1.33 g/L in this study to achieve the highest shear strength, which was confirmed with the densest microstructure in the SEM and a series of stronger and wider cementitious peaks of cementitious products in the X-ray diffraction 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 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 score0.113
Threshold uncertainty score0.505

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 head0.008
GPT teacher head0.223
Teacher spread0.214 · 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 teacher head, 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

Citations13
Published2020
Admission routes2
Has abstractyes

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