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Record W4280520692 · doi:10.1139/cgj-2021-0565

Suffusion of sand–clay mixture by three-staged change of ionic strength

2022· article· en· W4280520692 on OpenAlexvenueno aff
Jongmuk Won, Taehyeong Kim, Min Kyu Kang, Yongjoon Choe, Hangseok Choi

Bibliographic record

VenueCanadian Geotechnical Journal · 2022
Typearticle
Languageen
FieldEngineering
TopicDam Engineering and Safety
Canadian institutionsnot available
FundersMinistry of Science and ICT, South KoreaNational Research Foundation of KoreaNational Research Foundation
KeywordsKaoliniteMontmorilloniteClay mineralsIlliteExpansive clayGeotechnical engineeringHydraulic conductivityClay soilSoil waterIonic strengthGeologyMaterials scienceMineralogyComposite materialChemistrySoil science

Abstract

fetched live from OpenAlex

The suffusion of particles classified as coarse-grained soils in the Unified Soil Classification System has been relatively well investigated in previous studies, whereas suffusion of clay particles has not been thoroughly explored. For clay particles, the surface interaction energy between clay and sand can be a critical factor in conjunction with hydraulic gradient. Therefore, this paper presented a series of designed soil-column experiments for assessing the reduction of ionic strength on suffusion of clay particles in the sand–clay mixture. The three most typical clay particles (kaolinite, illite, and montmorillonite) were selected and the ionic strength was decreased during the injection. The results indicate that the sequence of decreasing ionic strength induced substantial suffusion of clay particles, mainly attributed to the decreased attraction between sand grains and clay particles. In addition, among three types of clay particles, the high swelling potential of montmorillonite particles led to the most substantial increase in relative hydraulic conductivity caused by suffusion.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.402
Threshold uncertainty score0.549

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 integrity0.0000.001
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 head0.009
GPT teacher head0.185
Teacher spread0.176 · 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 designSimulation or modeling
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

Citations8
Published2022
Admission routes1
Has abstractyes

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