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Mobility and Transport of Inorganic Species in Weathered Hydraulic Disposed Coal Fly Ash: An Insight from Geochemical Fractionation and Statistical Evalu- ation

2012· article· en· W1824675809 on OpenAlexvenueno aff
Segun A. Akinyemi, Olumuyiwa Idowu Ojo, Wilson M. Gitari, A. Akinlua, Ojo O. Fatoba, Leslie Petrik

Bibliographic record

VenueEnergy science and technology · 2012
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCoal and Its By-products
Canadian institutionsnot available
Fundersnot available
KeywordsFly ashWeatheringChemical compositionCoalEnvironmental chemistryFractionationExtraction (chemistry)GeologyChemistryMineralogyGeochemistry

Abstract

fetched live from OpenAlex

A large volume of coal fly ash generated through combustion process has raised environmental concerns due to possible release of potentially toxic species to the surface and groundwater systems. The chemical partitioning and mobility of elements in the hydraulic disposed ash dump was investigated using modified sequential extraction scheme. The geochemical distribution of the investigated elements in 33 drilled core samples was determined by x-ray fluorescence and inductively coupled plasma mass spectrometry. The ternary plot of major elements as determined by XRF showed that hydraulic disposed ash cores are sialic, ferrosialic and ferrocalsialic in chemical composition. The relationship between SiO2 and chemical index of alteration (CIA) showed low, moderate to high degree of weathering. These chemical compositions and degree of chemical weathering depend on the ash sampling point and ash interaction chemistry. The Na + and K

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

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)0.0000.000

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 head0.011
GPT teacher head0.207
Teacher spread0.196 · 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 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
Published2012
Admission routes1
Has abstractyes

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