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Record W4253480921 · doi:10.1002/srin.200405951

Thermodynamic Activities of “FeO” in some Binary “FeO”‐Containing Slags

2004· article· en· W4253480921 on OpenAlexaff
Patrik Fredriksson, Seshadri Seetharaman

Bibliographic record

Venuesteel research international · 2004
Typearticle
Languageen
FieldEngineering
TopicMetallurgical Processes and Thermodynamics
Canadian institutionsRoyal Ottawa Mental Health Centre
Fundersnot available
KeywordsSlag (welding)Crucible (geodemography)Partial pressureOxideThermodynamicsPyrometallurgyMetallurgyPhase (matter)Materials scienceAtmospheric temperature rangeChemistryMetalOxygen

Abstract

fetched live from OpenAlex

In the present investigation, experimental measurements of the thermodynamic activities of iron oxide in the Al 2 O 3 ‐“FeO”, CaO‐“FeO” and “FeO”‐SiO 2 systems were performed in the temperature range 1823‐1873 K by using gas equilibration technique. The molten slag, kept in a Pt‐crucible was brought to equilibrium with a gas mixture of known oxygen partial pressure. A part of the Fe from the “FeO” was reduced during the equilibration and got dissolved in the Pt phase. The samples were quenched after the required equilibration time and the slag phase as well as the platinum crucible was subjected to chemical analysis. The activities of “FeO” in the slag were calculated from the experimental data using thermodynamic information on the Fe‐Pt binary metallic system generated and assessed earlier. The experimental results are compared with earlier thermodynamic studies of the slag systems. Reassessment with the KTH slag model is performed and the results are compared with other thermodynamic models, viz. F*A*C*T™ and Thermo‐Calc™ respectively. The experimental activities predicted by the KTH slag model are in good agreement with the experimental data available in the literature. A general agreement between the various models is also observed.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.596
Threshold uncertainty score0.447

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.037
GPT teacher head0.327
Teacher spread0.291 · 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

Citations24
Published2004
Admission routes1
Has abstractyes

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