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Record W4407928632 · doi:10.1016/j.mtcomm.2025.112024

Evolution of carbon distribution across meso-scale segregations in pressure vessel steel

2025· article· en· W4407928632 on OpenAlexaff
Marion Bregeault, Arthur Marceaux dit Clément, Brendan Le Gloannec, François Roch, M. Véron, H.P. Van Landeghem

Bibliographic record

VenueMaterials Today Communications · 2025
Typearticle
Languageen
FieldEngineering
TopicMicrostructure and Mechanical Properties of Steels
Canadian institutionsFraser Health
Fundersnot available
KeywordsMaterials sciencePressure vesselScale (ratio)Carbon steelCarbon fibersDistribution (mathematics)MetallurgyComposite materialComposite number

Abstract

fetched live from OpenAlex

Carbon plays a major role in the microstructural evolution and associated mechanical properties of nuclear pressure vessel steel. Solidification conditions of large ingots lead to an inhomogeneous distribution of all elements in the microstructure. The standard thermal schedule for pressurized components was found to afford carbon enough mobility for complete meso-scale homogenization, while substitutional elements remain unaffected. Their inhomogeneous distribution negligibly changes the activity of carbon in austenite. Subsequent bainitic transformation causes redistribution of carbon at the scale of the parent austenite grain only. Tempering, however, leads to migration of carbon from solute-lean to solute-rich regions depending on its duration. • Carbon potential is homogenous across the microstructure during reaustenitization. • Reaustenitization provides carbon with sufficient time and mobility to homogenize. • Chomium and molybdenum segregations cause potential well in ferritic microstructures. • Increased carbide precipitation is the main driver of the carbon potential decrease. • As a result, carbon segregates back along substitutional elements during tempering.

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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.055
Threshold uncertainty score0.475

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 head0.009
GPT teacher head0.241
Teacher spread0.232 · 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 designBench or experimental
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

Citations2
Published2025
Admission routes1
Has abstractyes

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