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Record W4396708523 · doi:10.59957/jctm.v59.i3.2024.14

APPLICATION OF SPARK PLASMA SINTERING AS A METHOD FOR PRODUCING NEW CERAMIC MATERIALS FROM SILICON PRODUCTION WASTE

2024· article· en· W4396708523 on OpenAlex
Gulzhainat Akhmetova, G. A. Ulyeva, K. Tuyskhan, Irina Volokitina

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Chemical Technology and Metallurgy · 2024
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced ceramic materials synthesis
Canadian institutionsArcelorMittal (Canada)
Fundersnot available
KeywordsSpark plasma sinteringCeramicMaterials scienceSiliconPlasmaSinteringProduction (economics)SPARK (programming language)Process engineeringWaste managementMetallurgyComputer scienceEngineering

Abstract

fetched live from OpenAlex

The article presents some existing methods of producing silicon carbide, forms and types of silicon carbide, various compositions and formulations, production technology. The authors proposed a new method of processing silicon production waste - microsilica by spark plasma sintering with a carbon-containing reducing agent (soot) which produces a silicon carbide compound. Studies have shown that sintered silicon carbide has improved strength properties. Thus, the possibility of application of production wastes to create new composite materials characterized by a certain complex of properties is confirmed.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.049
Threshold uncertainty score0.552

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.281
Teacher spread0.270 · 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