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USE OF WASHING BRIQUETTES FOR IMPROVEMENT OF THE BLAST-FURNACE HEARTH OPERATION

2015· article· en· W2271297052 on OpenAlexaff
Vladimir Alexandrovich Dolinskiy, L. D. Nikitin, A. M. Коverzin, L. V. Portnov, S. F. Bugayev

Bibliographic record

VenueIzvestiya Ferrous Metallurgy · 2015
Typearticle
Languageen
FieldEngineering
TopicIron and Steelmaking Processes
Canadian institutionsEVRAZ (Canada)
Fundersnot available
KeywordsBlast furnaceHearthPig ironCokeBriquettePelletsMetallurgyPelletRacewayStoveEnvironmental scienceWaste managementMaterials scienceCoalEngineeringComposite material

Abstract

fetched live from OpenAlex

The use of washing pellets in the amount of 30 – 60 kg/t of pig iron in blast furnace with useful volume of 3000 m3 has contributed to the quite effective flushing of the horn and preservation of the smooth furnace operation. The estimated reduced productivity of the blast furnaces increased by 3,5 % while reducing the specific con-sumption of coke reduced by 0,69 %.

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)0.001
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 score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.075
GPT teacher head0.242
Teacher spread0.167 · 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

Citations1
Published2015
Admission routes1
Has abstractyes

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