MétaCan
Menu
Back to cohort
Record W1972191759 · doi:10.1002/crat.200410344

Recent developments in modelling of liquid phase electroepitaxy under applied magnetic field

2005· article· en· W1972191759 on OpenAlexaff
S. Dost, Hamdi Sheibani, Y. C. Liu, B. Lent

Bibliographic record

VenueCrystal Research and Technology · 2005
Typearticle
Languageen
FieldMaterials Science
TopicSolidification and crystal growth phenomena
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsMagnetic fieldPhase (matter)Mixing (physics)Condensed matter physicsRotating magnetic fieldMaterials scienceField (mathematics)Intensity (physics)Field intensityGrowth rateMagnetostaticsConvectionThermodynamicsChemistryNuclear magnetic resonanceOpticsPhysicsMathematics

Abstract

fetched live from OpenAlex

Abstract Growth of single crystals under magnetic field is of interest for suppressing the adverse effect of natural convection and for better mixing in the liquid solution, which are the favorable conditions for prolonged growth and high quality crystals. In this short review article, recent developments in the modelling of electroepitaxy under magnetic field are presented. An application is given for the liquid phase electroepitaxial growth of GaAs bulk single crystals under a static magnetic field. Experimental results, that have shown that the growth rate under an applied static magnetic field is proportional to the applied magnetic field, and increases with the field intensity level, are predicted from the present model. The model also predicts growth interface shapes accurately. (© 2005 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)

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)0.001
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: Simulation or modeling
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
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 head0.072
GPT teacher head0.341
Teacher spread0.269 · 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
GenreReview

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

Citations4
Published2005
Admission routes1
Has abstractyes

Explore more

Same venueCrystal Research and TechnologySame topicSolidification and crystal growth phenomenaFrench-language works237,207