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Record W2390849225

Effect of Various Fly Ash Compositions on Mercury Speciation Transformation

2010· article· en· W2390849225 on OpenAlex
Jinjing Luo

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueProceedings of the CSEE · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsnot available
Fundersnot available
KeywordsFly ashMercury (programming language)Environmental chemistryChemistryParticle sizeMetallurgyEnvironmental scienceMineralogyMaterials science
DOInot available

Abstract

fetched live from OpenAlex

Fly ash samples were derived from the 1st electrostatic precipitators of Songyu power plant in Xiamen,and different particle sizes of samples were screened out,the compositions of fly ash has been studied with X-ray fluorescence spectrometry.Using Ontario Hydro method for mercury speciation testing,a bench-scale testing apparatus was constructed to study the effect of various fly ash compositions on mercury speciation transformation.The conclusions are as follows: The main compositions of fly ash are: Al2O3,SiO2,MgO,CaO and Fe2O3,all of them could enhance oxidation of Hg,especially Al2O3 and CaO.The size of fly ash has impact on the mercury transformation,and the conversion ratio of Hg2+ to total Hg increased with increasing the size of fly ash sample.Experiment results show that O2 and HCl can greatly promote the mercury oxidization.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.131
Threshold uncertainty score0.203

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)0.0000.000

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 head0.005
GPT teacher head0.234
Teacher spread0.228 · 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