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Record W3038290960 · doi:10.3390/min10070596

Hydrogeochemical Behavior of Reclaimed Highly Reactive Tailings, Part 1: Characterization of Reclamation Materials

2020· article· en· W3038290960 on OpenAlexafffund
Alex Kalonji-Kabambi, Bruno Bussière, Isabelle Demers

Bibliographic record

VenueMinerals · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicMine drainage and remediation techniques
Canadian institutionsNatural Sciences and Engineering Research Council of CanadaUniversité du Québec en Abitibi-Témiscamingue
FundersNatural Sciences and Engineering Research Council of CanadaMitacs
KeywordsTailingsLand reclamationAcid mine drainageContext (archaeology)Environmental scienceMining engineeringExtraction (chemistry)Municipal solid wasteDrainageWaste managementGeologyEnvironmental chemistryEngineeringMetallurgyChemistryMaterials scienceArchaeology

Abstract

fetched live from OpenAlex

The production of solid mine wastes is an integral part of the extraction and metallurgical processing of ores. The reclamation of highly reactive mine waste, with low neutralizing potential, is still a significant challenge for the mining industry, particularly when natural soils are not available close to the site. Some solid mine wastes present interesting hydro-geotechnical properties which can be taken advantage of, particularly for being used in reclamation covers to control acid mine drainage. The main objective of this research was to evaluate the use of mining materials (i.e., tailings and waste rock) in a cover with capillary barrier effects (CCBE) to prevent acid mine drainage (AMD) from highly reactive tailings. The first part of the project reproduced in this article involves context and laboratory validation of mining materials as suitable for a CCBE, while the companion paper reports laboratory and field results of cover systems made with mining materials. The main conclusions of the Part 1 of this study were that the materials studied (low sulfide tailings and waste rocks) had the appropriate geochemical and hydrogeological properties for use as cover materials in a CCBE. Results also showed that the cover mining materials are not acid-generating and that the LaRonde tailings are highly reactive with pH close to 2, with high concentrations of metals and sulfates.

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 score0.002
Threshold uncertainty score1.000

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)0.0010.000

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 head0.016
GPT teacher head0.227
Teacher spread0.212 · 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

Citations7
Published2020
Admission routes2
Has abstractyes

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