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Record W2286423074 · doi:10.1680/envgeo.13.00095

Characterisation of bentonite polymer for bottom liner use

2014· article· en· W2286423074 on OpenAlexaff
Andry Razakamanantsoa, Irini Djéran‐Maigre, Gilles Barast

Bibliographic record

VenueEnvironmental Geotechnics · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicLandfill Environmental Impact Studies
Canadian institutionsUniversité du Québec en Abitibi-Témiscamingue
FundersInstitut National des Sciences Appliquées de Lyon
KeywordsBentonitePolymerSwellingSwellPermeability (electromagnetism)AdsorptionMaterials scienceHydraulic conductivityLeachatePolyelectrolyteChemical engineeringGeotechnical engineeringComposite materialEnvironmental scienceGeologyChemistrySoil waterSoil scienceMembraneOrganic chemistryEnvironmental chemistry

Abstract

fetched live from OpenAlex

This paper aims to study the hydromechanical behaviour and the clay polymer interaction of amended Ca-bentonite. Specimens were formed by mixing Ca-bentonite with two soluble polyelectrolyte polymer powders. Some important parameters are studied: swelling, water adsorption and hydraulic performance for landfill applications. Tests are performed with tap water and synthetic leachate (SL) to reproduce the hydrochemical phenomena. Hydraulic performance tests were performed with an oedopermeameter. Tests results show that polymers tend to reduce the permeability when in contact with the SL. Water adsorption and free swell index tests confirmed that adsorption, swelling and permeability parameters depend on the clay polymer mixtures and that adding polymers improves the clay properties. Each polymer’s charges has a different effect: the anionic polymer gives low permeability to the mixture, and the cationic polymer enhances the bentonite swell ability and water retention, which can be used also as a performance index.

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 score0.001
Threshold uncertainty score0.004

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)0.0010.000

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 head0.010
GPT teacher head0.205
Teacher spread0.195 · 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

Citations26
Published2014
Admission routes1
Has abstractyes

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