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Record W2101808175 · doi:10.1139/s08-019

Characterization and environmental evaluation of Atikokan coal fly ash for environmental applications

2008· article· en· W2101808175 on OpenAlexafffundvenueabout
Muluken B. Yeheyis, Julie Q. Shang, Ernest K. Yanful

Bibliographic record

VenueJournal of Environmental Engineering and Science · 2008
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCoal and Its By-products
Canadian institutionsWestern University
FundersNatural Resources CanadaNatural Sciences and Engineering Research Council of Canada
KeywordsFly ashLeachateEnvironmental scienceCoalLeaching (pedology)Waste managementCalciteWeatheringEnvironmental chemistryMineralogyGeologySoil waterChemistryGeochemistrySoil science

Abstract

fetched live from OpenAlex

This study presents the results of physical, chemical, and mineralogical characterization of fresh and landfilled coal fly ash from Atikokan Thermal Generating Station, Ontario. The acid neutralization capacity, heavy metal sorption capacity, and possible environmental impact under different environmental conditions during utilization of the coal fly ash were also evaluated. The results show significant variations in morphology, mineralogy, and chemical composition between fresh and landfilled fly ashes attributed largely to weathering of the coal fly ash during landfilling. The formation of secondary minerals (predominantly calcite) in landfilled fly ash samples is confirmed by X-ray diffraction, thermal analysis, and Chittick tests. Chemical analysis of the generalized acid neutralization leaching test indicated that the heavy metals from both fresh and landfilled fly ash samples were below the leachate criteria set by the Ontario Government. Despite variations in acid neutralization capacity and physical and geochemical behaviors between fresh and landfilled coal fly ash materials, the overall results of this study suggest both materials have favorable engineering and environmental properties that make them suitable for various environmental applications.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.839
Threshold uncertainty score0.340

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 studies0.0000.000
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 head0.012
GPT teacher head0.179
Teacher spread0.167 · 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 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
Published2008
Admission routes4
Has abstractyes

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