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Recommandations sur l’utilisation des outils de prédiction du drainage minier acide

2014· article· en· W3135786173 on OpenAlexafffund
Hassan Bouzahzah, Mostafa Benzaazoua, Bruno Bussière, Benoît Plante

Bibliographic record

VenueEnvironnement Ingénierie & Développement · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicMine drainage and remediation techniques
Canadian institutionsUniversité du Québec en Abitibi-Témiscamingue
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTailingsDictionAcid mine drainageSulfurChemistryHumanitiesEnvironmental scienceForestryMineralogyEnvironmental chemistryGeographyArtPhysical chemistry

Abstract

fetched live from OpenAlex

The sulphidic tailings subjected to the atmospheric water and oxygen action may generate acid mine drainage (AMD). AMD is due to the oxidation of sulphide minerals they contain in the absence of a neutralizing potential. The effluents related to AMD are acidic, metal loaded and are often harmful towards the surrounding environments. A reliable prediction of this phenomenon to preserve the environment is of great importance as it is able to impact the costs and the ways to restore mines sites. The most frequently tools used for AMD prediction are static tests and kinetic tests when the first provide uncertain prediction results. AMD perdition tests are available in several versions whose protocols are quite divergent. Also, there is no guide orientating users in their choice. Thus, the objective of this review paper is to give clear recommendations to help choosing among either static and kinetic tests for AMD prediction based on tailings intrinsic characteristics (for static tests) and on the objectives of the study (for kinetic assays). Les rejets miniers sulfurés soumis à l’action de l’eau et de l’oxygène atmosphériques sont susceptibles de générer du drainage minier acide (DMA) suite à l’oxydation des minéraux sulfurés qu’ils contiennent, en l’absence d’un potentiel neutralisant. Les effluents acides liés au DMA peuvent être chargés en métaux et sont dommageables à l’environnement. Une prédiction fiable de ce phénomène est d’une grande importance car elle est capable de déterminer les coûts et la manière de restaurer les sites miniers pour préserver l’environnement. Les outils usuellement utilisés pour la prédiction du DMA sont les tests statiques dans un premier temps et les essais cinétiques quand les premiers livrent une prédiction incertaine. Ces outils sont nombreux, leurs protocoles sont assez distincts et il n’existe aucun guide pour orienter l’utilisateur dans leur choix. Ainsi, ce papier se fixe l’objectif de présenter des recommandations pour orienter le choix parmi les tests statiques et et les essais cinétiques. Ces outils sont utilisés par l’industrie minière pour la prédiction du DMA en se basant sur les caractéristiques intrinsèques des rejets miniers (tests statiques) et des objectifs de l’étude (essais cinétiques).

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.048
metaresearch head score (Gemma)0.114
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.048
Threshold uncertainty score0.252

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0480.114
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0040.003
Science and technology studies0.0010.002
Scholarly communication0.0100.005
Open science0.0050.003
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0060.006

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 head0.233
Teacher spread0.219 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreMethods

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

Citations2
Published2014
Admission routes2
Has abstractyes

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