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Record W4378836266 · doi:10.17073/1683-4518-2023-1-3-7

Material composition and microstructure of ceramic glass й hearth of blast furnace No. 6 of JSC EVRAZ NTMK after service. Refractory microstructure after service

2023· article· en· W4378836266 on OpenAlex

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

VenueNOVYE OGNEUPORY (NEW REFRACTORIES) · 2023
Typearticle
Languageen
FieldEngineering
TopicIron and Steelmaking Processes
Canadian institutionsEVRAZ (Canada)
Fundersnot available
KeywordsMicrostructureMulliteMaterials scienceMetallurgyBlast furnaceCeramicEutectic systemSilicon carbideCarbideComposite material

Abstract

fetched live from OpenAlex

The results of a comprehensive study of the material microstructure of refractory samples after service in the furnace of a blast furnace with a useful volume of 2200 m3 for 14 years are presented. Structural and genetic analysis established a characteristic sequence of processes of degeneration and wear of the ceramic furnace stack, including 6 stages: the formation of micro-and macro-cracks; condensation of vaporous zinc; zinc oxidation with the formation of fire-resistant ZnO zincite and the deposition of carbon black by the; chemical interaction of zinkite with mullite and corundum to form ZnAl2O4 ganite and 2ZnO·SiO2 willemite; partial oxidation of silicon carbide with the release of silica glass SiO2, eutectic melt of complex composition, and wellimite. Due to intensive accumulation of capillary pores, infiltration of the slag melt in the volume of refractories does not occur. Wear of refractories in the furnace has a complex, mainly thermochemical mechanism and a low speed due to the formation of a garnish containing refractory compounds. Ill. 5. Ref. 14.

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 categoriesMeta-epidemiology (narrow)
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 score0.500
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.006
GPT teacher head0.209
Teacher spread0.203 · 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