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Record W2002768723 · doi:10.1021/es0348400

A Two-Front Leach Model for Cement-Stabilized Heavy Metal Waste

2004· article· en· W2002768723 on OpenAlexaff
Mohammad Zahirul Islam, Lionel J.J. Catalan, Ernest K. Yanful

Bibliographic record

VenueEnvironmental Science & Technology · 2004
Typearticle
Languageen
FieldEngineering
TopicConcrete and Cement Materials Research
Canadian institutionsLakehead UniversityWestern University
Fundersnot available
KeywordsLeaching (pedology)PortlanditeDissolutionLeachateCementMetalCalcium silicate hydrateChemistryLessivageScanning electron microscopeMetallurgyMineralogyMaterials sciencePortland cementGeologyEnvironmental chemistryComposite materialSoil water

Abstract

fetched live from OpenAlex

Quantitative scanning electron microscope (SEM) studies of cement-stabilized waste specimens exposed to a leaching solution at constant pH in the range 4-7 have shown that the acid neutralization capacity (ANC) of the waste matrix is consumed at two consecutive leaching fronts. The first front is associated with the dissolution of portlandite (Ca(OH)2) and the partial reaction of calcium silicate hydrate (CSH) gel. The second front marks the dissolution of Ca-Al hydroxy sulfate minerals. The advancement of the first front is limited by the diffusion of OH- ions from the first front toward the leaching solution. The advancement of the second front, however, is controlled by the diffusion of H+ ions from the leaching solution toward the second front. Leaching of copper, zinc, and lead only occurs between the second front and the specimen surface. The leaching behavior of metals is modeled by considering that metals are leached from the waste matrix as a result of the advancement of the second front. The proposed model takes into account the leachable metal fraction in the waste matrix and the effect of metal remineralization on metal mobility.

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 score0.112
Threshold uncertainty score0.634

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.014
GPT teacher head0.244
Teacher spread0.230 · 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

Citations22
Published2004
Admission routes1
Has abstractyes

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