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Record W2748362289 · doi:10.3233/jae-172252

Relevance of microstructure and texture to the accuracy and interpretation of 1 and 2 directional characterisation and testing of grain-oriented electrical steels

2017· article· en· W2748362289 on OpenAlexaff
A.J. Moses

Bibliographic record

VenueInternational Journal of Applied Electromagnetics and Mechanics · 2017
Typearticle
Languageen
FieldMaterials Science
TopicMagnetic Properties and Applications
Canadian institutionsTellabs (Canada)
Fundersnot available
KeywordsElectrical steelEddy currentHarmonicsMicrostructureAnisotropyTexture (cosmology)Flux (metallurgy)MagnetizationMaterials scienceHysteresisTransverse planeField (mathematics)Relevance (law)Condensed matter physicsMetallurgyMagnetic fieldVoltageComputer scienceStructural engineeringEngineeringElectrical engineeringPhysicsMathematicsOpticsArtificial intelligence

Abstract

fetched live from OpenAlex

This paper is intended to stimulate discussion of some effects of not properly accounting for material microstructure in some established methods of measurement and prediction of losses in electrical steel laminations. Aspects of methods which have been used for many years are briefly discussed and some constraints or pitfalls are raised which users should be aware of. The basic cause of losses is summarised in order to set the scene for discussion of the analysis of losses into classical eddy current, hysteresis and anomalous loss. The possible presence and consequences of transverse flux in grain-oriented (GO) steels is raised followed by an explanation of some effects of the strong anisotropy of GO steel on flux density, magnetic field and loss measurement in stacks and single strips of GO steel. The presentation concludes with some questions on how circular rotational magnetisation can be made and precautions needed when assessing the effect of flux harmonics on the losses.

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 score0.075
Threshold uncertainty score0.261

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 head0.008
GPT teacher head0.247
Teacher spread0.239 · 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

Citations7
Published2017
Admission routes1
Has abstractyes

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