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Record W2061116530 · doi:10.5539/ijc.v3n3p75

Treatments of Can Lignite with Some Mineral Acids after Sodyum Hydroxide Washing

2011· article· en· W2061116530 on OpenAlexvenueno aff
J̇ale Gülen, Sabriye Pişkin, İ̇brahim Doymaz

Bibliographic record

VenueInternational Journal of Chemistry · 2011
Typearticle
Languageen
FieldMaterials Science
TopicEngineering and Material Science Research
Canadian institutionsnot available
Fundersnot available
KeywordsChemistryMineralSulfurMineral acidHydroxideFourier transform infrared spectroscopyNuclear chemistryInorganic chemistryOrganic chemistryChemical engineeringAqueous solution

Abstract

fetched live from OpenAlex

The fossil properties can be increased via enrichment process. It is the process that removes sulfur and mineral matter without changing the organic structure. In this study, can lignite was demineralized with some mineral free acid of 5% such as HCOOH and some mineral acids of 5% such as HNO3, H2SO4, HCl, H3PO4 and HF after 5% NaOH washing. The best sulfur and mineral matter performed were obtained with NaOH plus HF treatment as approximately 44% and 45%, respectively. FTIR and X ray spectra of the samples were also drawn to determine the functional groups and mineral composition.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

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)0.0010.000

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 head0.012
GPT teacher head0.248
Teacher spread0.236 · 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 designBench or experimental
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

Citations3
Published2011
Admission routes1
Has abstractyes

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