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Modelling Phase Transformation Kinetics in Fe–Mn Alloys

2012· article· en· W1975859886 on OpenAlex
Tao Jia, Matthias Militzer

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 · 2012
Typearticle
Languageen
FieldEngineering
TopicMicrostructure and Mechanical Properties of Steels
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSolvent dragAusteniteMaterials scienceThermodynamicsAlloyFerrite (magnet)Thermal diffusivityKineticsGrain boundaryMetallurgyDiffusionDragPhase (matter)MicrostructureChemistryComposite material

Abstract

fetched live from OpenAlex

A unified model is proposed for the austenite-to-ferrite transformation kinetics in binary Fe–Mn alloys that accounts for solute drag of Mn. To aid the model development, continuous cooling transformation (CCT) tests were conducted for an interstitial-free steel that can be considered as Fe–0.1%Mn alloy. The experimental transformation data are supplemented with literature data for Fe–1%Mn and Fe–2%Mn alloys to establish a CCT database for Fe–Mn alloys. The austenite-to-ferrite transformation kinetics is described from a fundamental perspective by assuming an interface-controlled reaction and including solute drag of Mn. Using the solute drag model of Fazeli and Militzer, intrinsic interface mobility, trans-interface diffusivity of Mn and its binding energy have been determined from the CCT data. The interfacial parameters are critically analyzed and compared with independent measurements of diffusion and grain boundary segregation.

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: Empirical
Teacher disagreement score0.272
Threshold uncertainty score0.280

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 head0.023
GPT teacher head0.246
Teacher spread0.224 · 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