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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 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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.002
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0110.003

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; 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 designObservational
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