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Record W3134430558 · doi:10.3390/min11030280

Geochemical Assessment of Desulphurized Tailings as Cover Material in Cold Climates

2021· article· en· W3134430558 on OpenAlexafffundabout
Asif Ali Qureshi, Bruno Bussière, Mostafa Benzaazoua, Fannie Lessard, Vincent Boulanger-Martel

Bibliographic record

VenueMinerals · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicMine drainage and remediation techniques
Canadian institutionsUniversité du Québec en Abitibi-Témiscamingue
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of WaterlooCanadian Light Source
KeywordsTailingsLeaching (pedology)Acid mine drainageLeachateEnvironmental scienceLand reclamationGeologyEnvironmental chemistrySoil waterMetallurgyChemistrySoil scienceMaterials science

Abstract

fetched live from OpenAlex

It is essential to develop effective mine waste management approaches and mine site reclamation techniques to curtail the adverse effects of mining processes on the natural environment. This study focuses on the use of partially desulphurized tailings as a moisture-retaining layer in an insulation cover with capillary barrier effects (ICCBE). Tailings were obtained from a nickel ultramafic ore processing plant at a mining company located in a continuous permafrost region of northern Québec, Canada. The geochemical response of tailings at two different sulphur contents (0.4 and 0.8 wt%), with and without ICCBEs, was tested by applying eight freeze-thaw and wetting cycles. Desulphurization of the tailings allowed to reduce the content of sulphide minerals by about 90%, from ~22 wt% to around 1.2–2.2 wt%. Column kinetic geochemical tests showed that Ni leaching was significantly reduced to concentrations ranging between 0.01–0.22 mg L−1 compared to 0.63–1.92 mg L−1 from the raw tailings (thanks to the desulphurization process). Zinc release was maintained around 0.04–1.72 mg L−1 compared to 0.4–3.69 mg L−1 from the raw tailing. Although all the columns produced leachates displaying circumneutral to slightly alkaline pH, the columns with ICCBE are expected to prevent acid mine drainage generation longer than the other columns due to reduced sulphide content and a constantly high degree of saturation maintained by capillary barrier effects.

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 categoriesInsufficient payload (model declined to judge)
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 score0.991

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)0.0100.000

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 head0.008
GPT teacher head0.272
Teacher spread0.264 · 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.

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

Citations4
Published2021
Admission routes3
Has abstractyes

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