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Record W2086724546 · doi:10.3103/s1068364x14070084

Assessing the condition of coke

2014· article· en· W2086724546 on OpenAlexaff
Yu. V. Stepanov, V. S. Yakimov

Bibliographic record

VenueCoke and Chemistry · 2014
Typearticle
Languageen
FieldEnergy
TopicCoal and Coke Industries Research
Canadian institutionsEVRAZ (Canada)
Fundersnot available
KeywordsCokeYield (engineering)Waste managementEnvironmental scienceMetallurgyProcess engineeringChemistryMaterials scienceEngineeringPulp and paper industry

Abstract

fetched live from OpenAlex

As shown by critical analysis, a recent article by Rubchevskii and his colleagues failed to adequately review methods of assessing the condition of coke [1]. A method developed in the central laboratory of the coke plant at OAO EVRAZ NTMK has long been used to assess the condition of coke in terms of the difference in the yield of volatiles (ΔV daf ) between coke breeze (V cb ) and coke (V c ). This method is simpler, more convenient, and more precise than the determination of the bulk yield of volatiles from coke.

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)0.003
metaresearch head score (Gemma)0.006
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: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.001
Science and technology studies0.0020.002
Scholarly communication0.0020.003
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.002

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 head0.024
GPT teacher head0.305
Teacher spread0.281 · 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

Citations1
Published2014
Admission routes1
Has abstractyes

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