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Record W2899590644 · doi:10.1088/1361-651x/aaef22

Ordering of carbon in highly supersaturated <i>α</i> -Fe

2018· article· en· W2899590644 on OpenAlexaff
Osamu Waseda, Julien Morthomas, Fabienne Ribeiro, Patrice Chantrenne, Chad W. Sinclair, Michel Perez

Bibliographic record

VenueModelling and Simulation in Materials Science and Engineering · 2018
Typearticle
Languageen
FieldEngineering
TopicAdvanced Materials Characterization Techniques
Canadian institutionsUniversity of British Columbia
FundersCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsMonte Carlo methodMaterials scienceMean field theoryCarbon fibersCarbideThermodynamicsStatistical physicsCondensed matter physicsPhysicsStatisticsMathematics

Abstract

fetched live from OpenAlex

Abstract Metropolis Monte Carlo is used to investigate the Zener ordering as the carbon content of body centered cubic iron is increased. Thanks to a fast simulation algorithm, the equilibrium state for a wide range of temperature and carbon content are investigated. These results are compared to a thermodynamical mean-field model that accounts for long range elastic interaction and configurational entropy. At carbon levels of above 2 at.%, it is found that the mean-field model overestimates the order–disorder transition temperature. This is due to local repulsive C–C interactions not accounted for in the mean-field model. Forbidding some strongly repulsive configurations leads to a better agreement between the mean-field model and Metropolis Monte Carlo simulations. At high concentration carbon atoms in solid solution exhibits local configurations typical of the Fe 16 C 2 carbide.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.008
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

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 communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.012
GPT teacher head0.223
Teacher spread0.211 · 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 source (direct Gemma or distilled Codex), 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

Citations7
Published2018
Admission routes1
Has abstractyes

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