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Record W2050666060 · doi:10.1139/t05-100

Utilization of Atikokan coal fly ash in acid rock drainage control from Musselwhite Mine tailings

2006· article· en· W2050666060 on OpenAlexfundvenueaboutno aff
H L Wang, Julie Q. Shang, K S Ho

Bibliographic record

VenueCanadian Geotechnical Journal · 2006
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCoal and Its By-products
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsFly ashTailingsLeachateAcid mine drainageLeaching (pedology)CoalCoal miningDrainageCementitiousHydraulic conductivityWaste managementMining engineeringEnvironmental scienceGeologyGeotechnical engineeringCementEnvironmental chemistryChemistrySoil waterMetallurgyMaterials scienceSoil scienceEngineering

Abstract

fetched live from OpenAlex

A site-specific study is carried out to assess the suitability of utilizing Atikokan coal fly ash (AFA) as a buffering material to control and mitigate the generation of acid rock drainage from reactive Musselwhite Mine tailings. The physical, chemical, and mineralogical properties of the fly ash and mine tailings are determined via experiments, followed by six kinetic column permeation tests to monitor the leaching properties of the coal fly ash and coal fly ash – mine tailings mixtures. The results of the experiments indicate that the hydraulic conductivities of high-calcium AFA and the ash–tailings mixtures are significantly reduced upon contact with acidic drainage. The pH of the pore fluid has increased from acidic (pH 4) to alkaline (pH 8 and above). Chemical analyses after the kinetic column permeation tests further indicate that concentrations of regulated elements in the leachate from the ash–tailings mixtures are well below the guideline limits set by the Ontario environmental authority for accelerated flow conditions.Key words: coal fly ash, mine tailings, hydraulic conductivity, pH, heavy metals, acid rock drainage.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.298
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 head0.011
GPT teacher head0.189
Teacher spread0.177 · 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

Citations26
Published2006
Admission routes3
Has abstractyes

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