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Record W2037742499 · doi:10.2298/jmmb120918039k

Modeling the viscosity of silicate melts containing manganese oxide

2013· article· en· W2037742499 on OpenAlexafffund
Wan-Yi Kim, A. D. Pelton, C. W. Bale, Ève Bélisle, Sergei A. Decterov

Bibliographic record

VenueJournal of Mining and Metallurgy Section B Metallurgy · 2013
Typearticle
Languageen
FieldEngineering
TopicMetallurgical Processes and Thermodynamics
Canadian institutionsPolytechnique Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSilicateViscosityTernary operationManganeseThermodynamicsOxideMaterials scienceManganese oxideMineralogyChemistryMetallurgyOrganic chemistryComputer sciencePhysics

Abstract

fetched live from OpenAlex

Our recently developed model for the viscosity of silicate melts is applied to describe and predict the viscosities of oxide melts containing manganese oxide. The model requires three pairs of adjustable parameters that describe the viscosities in three systems: pure MnO, MnO-SiO2 and MnO-Al2O3-SiO2. The viscosity of other ternary and multicomponent silicate melts containing MnO is then predicted by the model without any additional adjustable model parameters. Experimental viscosity data are reviewed for melts formed by MnO with SiO2, Al2O3, CaO, MgO, PbO, Na2O and K2O. The deviation of the available experimental data from the viscosities predicted by the model is shown to be within experimental error limits.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.093
Threshold uncertainty score0.727

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.013
GPT teacher head0.206
Teacher spread0.193 · 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 teacher head, 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

Citations9
Published2013
Admission routes2
Has abstractyes

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