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Record W2946516645 · doi:10.3390/min9050314

In Situ Effectiveness of Alkaline and Cementitious Amendments to Stabilize Oxidized Acid-Generating Tailings

2019· article· en· W2946516645 on OpenAlexafffundabout
Abdellatif Elghali, Mostafa Benzaazoua, Bruno Bussière, Thomas Genty

Bibliographic record

VenueMinerals · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicMine drainage and remediation techniques
Canadian institutionsUniversité du Québec en Abitibi-Témiscamingue
FundersNatural Sciences and Engineering Research Council of CanadaMitacs
KeywordsAmendmentTailingsPortland cementCementitiousMetalloidLeachateEnvironmental chemistryFly ashChemistryMetalCementMaterials scienceMetallurgyLawOrganic chemistry

Abstract

fetched live from OpenAlex

This study investigates the effectiveness of alkaline and cementitious additives in the in situ stabilization of localized acid-generating tailings from a closed gold mine in Abitibi–Témiscamingue, Québec (Eagle/Telbel mine site). Five field cells (including one control) were constructed and equipped with mechanisms for collecting vertical water infiltration and surface runoff. The five cells included: (C1) Control cell; (C2) 5 wt % limestone amendment; (C3) 10 wt % limestone amendment; (C4) 5 wt % half ordinary Portland cement and half fly ash amendment; and (C5) 5 wt % ordinary Portland cement amendment. The control cell showed an acidic behavior (pH < 4.5) with variable concentrations of Fe, Al, Zn, and Cu. The amendments were used to neutralize the acidic leachates and decrease dissolved metal concentrations. Leachates from surface runoff samples of amended cells were less loaded with metals compared to samples of vertical infiltration. All amendment formulations increased the pH of the leachates from approximately 4 to circumneutral values. Furthermore, metal and metalloid concentrations were greatly limited, except for Cr and As for the carbonate-based amendments. Metal(-oid) stabilization was successfully achieved using the different amendment formulations, with the exception of C2, which still released As.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Bench or experimentallow
gptno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Bench or experimentallow
models agreeAgreement compares identical category sets and study designs across arms.

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 score0.015
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.007
GPT teacher head0.249
Teacher spread0.242 · 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

Labeled directly by 2 models reading the full record.

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

Citations41
Published2019
Admission routes3
Has abstractyes

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