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Computer Applications of Thermodynamic Databases to Inclusion Engineering

2004· article· en· W2021984820 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueISIJ International · 2004
Typearticle
Languageen
FieldEngineering
TopicMetallurgical Processes and Thermodynamics
Canadian institutionsPolytechnique Montréal
FundersNatural Sciences and Engineering Research Council of CanadaMintek
KeywordsPhase diagramSpinelOxideSteelmakingCALPHADMaterials scienceInclusion (mineral)MetallurgySlag (welding)DiagramSolid solutionThermodynamicsPhase (matter)DatabaseChemistryComputer sciencePhysics

Abstract

fetched live from OpenAlex

Computerized thermodynamic databases for solid and liquid steel, slags and solid oxide solutions, for large numbers of components, have been developed over the last two decades by critical evaluation/optimization of all available phase equilibrium and thermodynamic data. The databases contain parameters of models specifically developed for molten slags; liquid and solid steel; and solid oxide solutions such as spinels. With user friendly software, which accesses these databases, complex equilibria involving slag, steel, inclusions, refractories and gases simultaneously, can be calculated for systems with many components, over wide ranges of temperature, oxygen potential and pressure. In the present article, several case studies will be presented, illustrating applications to complex steelmaking processes such as: Ca injection processes (Fe-Ca-Al-O inclusion diagram), corrosion of refractories, Mn/Si deoxidation, Ti/Al deoxidation (Fe-Al-Ti-O inclusion diagram), spinel formation (Fe-Mg-Al-O inclusion diagram), (Ti, N)(N, C) inclusion formation, oxide metallurgy.

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.

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)0.000
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: none
Teacher disagreement score0.827
Threshold uncertainty score0.310

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.007
GPT teacher head0.230
Teacher spread0.223 · 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