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Record W2965149626

Закономерности изменения качества кокса в зависимости от сырьевой базы ЦОФ “Кузнецкая” и ОУОУ ЕЗСМК

2017· article· ru· W2965149626 on OpenAlexaff
Ю. А. Золотухин, С. Н. Голубцов, К. П. Каракаш

Bibliographic record

VenueЧЕРНАЯ МЕТАЛЛУРГИЯ. Бюллетень научно-технической и экономической информации · 2017
Typearticle
Languageru
FieldEnergy
TopicCoal and Coke Industries Research
Canadian institutionsEVRAZ (Canada)
Fundersnot available
KeywordsRaw materialCoalEnvironmental scienceWaste managementBase (topology)Coal preparation plantPulp and paper industryMining engineeringEngineeringChemistryMathematics
DOInot available

Abstract

fetched live from OpenAlex

The results of the experimental investigations into the components of the coal charge materials included in the raw material base for coking at the Kuznetskaya Central Concentration Plant and EZSMK Coal Concentration Installation Department as well as the concentrates of the concentration plant included in the raw material base for coking at the AO EVRAZ ZSMK and other concentrates of the Kuznetsk Basin have been presented. The regularities in the formation of the indicators of the coke CSR/CRI depending on the variations of the raw material base at the Kuznetskaya Central Concentration Plant and EZSMK Coal Concentration Installation Department have been established.

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 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)0.006
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Open science, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Science and technology studies, Open science, Research integrity, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.564
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.007
Meta-epidemiology (narrow)0.0080.008
Meta-epidemiology (broad)0.0080.005
Bibliometrics0.0030.003
Science and technology studies0.0130.007
Scholarly communication0.0100.006
Open science0.0200.012
Research integrity0.0070.012
Insufficient payload (model declined to judge)0.0570.049

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 head0.091
GPT teacher head0.350
Teacher spread0.258 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations0
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueЧЕРНАЯ МЕТАЛЛУРГИЯ. Бюллетень научно-технической и экономической информацииSame topicCoal and Coke Industries ResearchFrench-language works237,207