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Record W3215322816 · doi:10.3390/min11121306

Incorporating Kinetic Modeling in the Development Stages of Hard Rock Mine Projects

2021· article· en· W3215322816 on OpenAlexafffund
Youssef Toubri, Denys Vermette, Isabelle Demers, Nicholas Beier, Mostafa Benzaazoua

Bibliographic record

VenueMinerals · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicMine drainage and remediation techniques
Canadian institutionsUniversity of AlbertaUniversité du Québec en Abitibi-Témiscamingue
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Waterloo
KeywordsWeatheringSilicateMineralGeochemical modelingSilicate mineralsLeachateClay mineralsGeologyCharacterization (materials science)MineralogyEnvironmental scienceGeochemistryEnvironmental chemistryChemistryChemical engineeringGroundwaterMaterials scienceGeotechnical engineeringMetallurgyEngineeringNanotechnology

Abstract

fetched live from OpenAlex

Weathering cell test, designed specifically to overcome material-limited constraints, yields prompt and efficient experimental assessment during the development stages of mining projects. However, it has barely benefited from geochemical modeling tools despite their ease of use. Accordingly, this paper aims to strengthen the upstream geochemical assessment via parametric analysis that simulates the effect of various mineral assemblages on leachate quality recovered from weathering cells. The main objective is to simulate the pH in presence of silicate neutralizing minerals and Mn release from carbonates based upon minimal characterization data. The public domain code PHREEQC was used for geochemical kinetic modeling of four weathering cells. The kinetic model utilized a water film concept to simulate diffusion of chemical elements from mineral surfaces to the pore water. The obtained results suggest that the presence of the silicate neutralizing minerals slightly affects the Mn release from carbonates. Furthermore, plagioclases could supply a significant neutralization potential when they predominate the mineral assemblage. Finally, coupling weathering cell test and parametric analyses illuminate the pH evolution for various mineral proportion scenarios.

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 score0.034
Threshold uncertainty score0.214

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 head0.040
GPT teacher head0.257
Teacher spread0.217 · 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

Citations7
Published2021
Admission routes2
Has abstractyes

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