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Record W2121599953 · doi:10.1111/jace.13688

Critical Evaluation and Thermodynamic Modeling of the MgO–MnO–Mn <sub>2</sub> O <sub>3</sub> –SiO <sub>2</sub> System

2015· article· en· W2121599953 on OpenAlexafffund
Sourav Kumar Panda, Zhanmin Cao, In‐Ho Jung

Bibliographic record

VenueJournal of the American Ceramic Society · 2015
Typearticle
Languageen
FieldEngineering
TopicMetallurgical Processes and Thermodynamics
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaJFE Steel CorporationVoestalpine
KeywordsLiquidusPhase diagramThermodynamicsGibbs free energyCALPHADThermodynamic databases for pure substancesOxideMaterials scienceThermodynamic processSolid solutionChemistryPhase (matter)Material propertiesMetallurgyPhysics

Abstract

fetched live from OpenAlex

A complete literature review, critical evaluation, and thermodynamic optimization of phase diagrams and thermodynamic properties of the MgO–MnO–Mn 2 O 3 –SiO 2 system at 1 atm pressure are presented. The molten oxide phase was described by the Modified Quasichemical Model considering the short‐range ordering in molten oxide, and the Gibbs energies of solid solutions were described using the Compound Energy Formalism considering the crystal structure of each solid solution. A set of optimized model parameters of all phases was obtained which reproduces all available and reliable thermodynamic data and phase diagrams within experimental error limits from 25°C to above the liquidus temperatures over the entire range of composition under the oxygen partial pressures from metallic saturation to 1 atm. The database of the model parameters can be used along with software for the Gibbs energy minimization to calculate any phase diagram section and thermodynamic property within the present system.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.017
GPT teacher head0.244
Teacher spread0.227 · 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 designSimulation or modeling
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

Citations11
Published2015
Admission routes2
Has abstractyes

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Same venueJournal of the American Ceramic SocietySame topicMetallurgical Processes and ThermodynamicsFrench-language works237,207