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Texture Development during Grain Growth in Nonoriented Electrical Steels

2005· article· en· W2089704539 on OpenAlex

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.

Bibliographic record

VenueISIJ International · 2005
Typearticle
Languageen
FieldMaterials Science
TopicMagnetic Properties and Applications
Canadian institutionsMcGill University
Fundersnot available
KeywordsMaterials scienceGrain sizeTexture (cosmology)Grain growthElectrical steelMetallurgyGrain boundaryAbnormal grain growthCore (optical fiber)Composite materialMicrostructureArtificial intelligence

Abstract

fetched live from OpenAlex

Nonoriented electrical steels should have both low core loss and high permeability. These magnetic properties are largely affected by grain size and texture. The research on grain size optimization during grain growth has been very extensive whereas little attention has been paid to texture transformation during grain growth. In the present study, based on obtained experimental results, a mechanism of texture development during grain growth in nonoriented electrical steels is proposed. In the 2% Si specimens, the major texture components, Goss and {111}‹112› components, are weakened during grain growth. In the 1% Si specimens, the main texture components, {111}‹112› and {111}‹110›, are strengthened. It is proposed that for a texture component to be continuously strengthened during grain growth, the grains of the specific orientation should have not only a size advantage over those of other orientations, but also a higher frequency of high angle, high energy grain boundaries.

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 categoriesInsufficient payload (model declined to judge)
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 score0.311
Threshold uncertainty score0.999

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)0.0020.000

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 head0.007
GPT teacher head0.227
Teacher spread0.221 · 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