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Record W3198554791 · doi:10.3390/min11090987

Valorisation of Partially Oxidized Tailings in a Cover System to Reclaim an Old Acid Generating Mine Site

2021· article· en· W3198554791 on OpenAlexaff
Gabrielle Dublet, Thomas Pabst, Gudny Okkenhaug, Christian Sætre, Anna Vårheim, Mari Tvedten, Samuel Kebede Gelena, Andreas Botnen Smebye, Marianne Kvennås, Gijs D. Breedveld

Bibliographic record

VenueMinerals · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicMine drainage and remediation techniques
Canadian institutionsPolytechnique Montréal
FundersNorges Forskningsråd
KeywordsTailingsLeachateAcid mine drainageEnvironmental scienceLand reclamationDrainageMoistureExtraction (chemistry)Environmental chemistryEnvironmental engineeringChemistry

Abstract

fetched live from OpenAlex

The reclamation of acid-generating mine tailings typically involves building cover systems to limit interactions with water or oxygen. The choice of cover materials is critical to ensure long-term performance, and partly determines the environmental footprint of the reclamation strategy. The objective of this research was to evaluate if tailings pre-oxidized on-site could be used in cover systems. Column experiments were performed to assess the effectiveness of a cover with capillary barrier effects (CCBE), where the moisture retention layer (MRL) was made of pre-oxidized tailings with little to no remaining sulfides (LS tailings). The columns were submitted to regular wetting and drying cycles, and their hydrological and geochemical behaviour was monitored for 510 days. The LS tailings showed satisfying hydrological properties as an MRL and remained saturated throughout the test. The concentrations of Cu in the drainage decreased by more than two orders of magnitude compared to non-covered tailings. In addition, the pH increased by nearly one unit compared to the control column, and Fe and S concentrations decreased by around 50%. Despite these improvements, the leachate water remained acidic and contaminated, indicating that acid drainage continued to be generated despite a hydrologically efficient CCBE.

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 score0.016
Threshold uncertainty score0.523

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 head0.014
GPT teacher head0.247
Teacher spread0.233 · 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 routes1
Has abstractyes

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