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Record W4382133401 · doi:10.11159/ijepr.2023.001

Restoration of Heterogeneous Soils Contaminated with Copper Using Electrokinetic Remediation

2023· article· en· W4382133401 on OpenAlexaffvenue
Ikrema Hassan, Eltayeb Mohamedelhassan

Bibliographic record

VenueInternational Journal of Environmental Pollution and Remediation · 2023
Typearticle
Languageen
FieldEngineering
TopicElectrokinetic Soil Remediation Techniques
Canadian institutionsLakehead UniversityUniversity of New Brunswick
Fundersnot available
KeywordsEnvironmental remediationElectrokinetic remediationCopperEnvironmental scienceContaminationSoil waterSoil contaminationSoil remediationEnvironmental chemistryElectrokinetic phenomenaChemistrySoil scienceMaterials scienceMetallurgyNanotechnologyBiologyEcology

Abstract

fetched live from OpenAlex

In this study, heterogeneous soils contaminated with copper were remediated using solar powered electrokinetic treatment.The heterogeneous soils were composed of clay and sand with ratio 2:1.In one soil, a sand layer was sandwiched between two layers of clay while in another sand pockets made 1/3 of the soil mass.The third heterogeneous soil was a claysand mixture.An additional test was carried out with homogeneous clay to provide data for contrast.The soil samples were artificially contaminated with 150 mg of copper per kg of dry soil at water content 41% and placed inside four identical electrokinetic cells.Each cell was connected to a solar cell panel with peak voltage gradient 205 V/m.Encouraging results were obtained.Eighty-seven percent of copper was removed from specimen near the anode in the test of the clay-sand mixture compared to 86% in the homogeneous clay.

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 score0.189
Threshold uncertainty score0.449

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 head0.007
GPT teacher head0.218
Teacher spread0.211 · 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

Citations0
Published2023
Admission routes2
Has abstractyes

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