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Record W2767182757

Mine Site Reclamation Challenge through Some Examples in Québec (Canada)

2017· article· en· W2767182757 on OpenAlexaboutno aff
Abdelkabir Maqsoud

Bibliographic record

VenueInternational Journal of Environmental Science · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicMine drainage and remediation techniques
Canadian institutionsnot available
Fundersnot available
KeywordsTailingsLand reclamationAcid mine drainageEnvironmental scienceWater tableDrainageEffluentSulfide mineralsEnvironmental engineeringWaste managementPyriteMining engineeringGroundwaterEnvironmental chemistryEngineeringGeologyChemistryMineralogyGeography
DOInot available

Abstract

fetched live from OpenAlex

The mining industry generates large amounts of solid and liquid waste. These wastes have the potential to adversely impact the environment if not properly managed. Special attention is required when the wastes contain sulfide minerals. The oxidation of sulfides minerals by atmospheric oxygen generates contaminant in the drainage water. This phenomenon is called acid mine drainage (AMD) when the effluents are acidic. In these situations, actions must be taken at the mine site to prevent environmental impacts caused by AMD. For that, reclamation of mine site constitutes the most important challenges for the mining industry and different techniques were developed to control the production of AMD. These techniques are used to eliminate, or to reduce to very low levels, the water flow (hydraulic barrier) and / or oxygen flux (oxygen barrier) to reactive tailings. Under humid climate conditions, the most appropriate techniques to control oxygen flux are: i) cover with capillary barrier effects (CCBE), and ii) monolayer cover with an elevated water table. These techniques were used for mine site reclamation in Abitibi-Temiscamingue (Quebec, Canada). The emphasis will be on their characteristics, configuration and performance.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.672
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.002
Open science0.0010.000
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 head0.015
GPT teacher head0.256
Teacher spread0.241 · 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 designObservational
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

Citations1
Published2017
Admission routes1
Has abstractyes

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