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Record W4283696129 · doi:10.1139/cgj-2021-0682

Role of ionic concentration in the kinetic attachment of kaolinite and sand

2022· article· en· W4283696129 on OpenAlexvenueno aff
Jongmuk Won, Susan E. Burns

Bibliographic record

VenueCanadian Geotechnical Journal · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicGroundwater flow and contamination studies
Canadian institutionsnot available
FundersNational Research Foundation of KoreaNational Research FoundationGeorgia Department of TransportationMinistry of Science and ICT, South KoreaU.S. Department of Transportation
KeywordsKaoliniteCloggingIonic strengthDeposition (geology)Particle (ecology)Particle sizeFiltration (mathematics)AdsorptionChemical engineeringPorous mediumMaterials scienceGeotechnical engineeringPorosityMineralogyChemistryComposite materialGeologySedimentAqueous solution

Abstract

fetched live from OpenAlex

Clay particle deposition in porous media is critical in many geotechnical applications that rely on filtration. The deposition of particles in the filter medium can result in clogging, which causes a reduction in the hydraulic conductivity of the filter medium. Particle attachment and detachment are a function of the interaction energy between clay particles and the filter bed material, which is frequently sand. These mechanisms are governed by the size of the clay particles as well as the solution chemistry. Batch kinetic adsorption tests were carried out to investigate the impact of ionic strength on the attachment of kaolinite to silica sand grains. Significant attachment of kaolinite was observed as ionic strength of the pore fluid increased and as the size of silica particles decreased. The short-term deposition of clay particles was not sensitive to ionic strength; however, in contrast, long-term deposition was a strong function of ionic strength.

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)0.001
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 score0.032
Threshold uncertainty score0.064

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
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 head0.008
GPT teacher head0.213
Teacher spread0.205 · 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

Citations3
Published2022
Admission routes1
Has abstractyes

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Same venueCanadian Geotechnical JournalSame topicGroundwater flow and contamination studiesFrench-language works237,207