Texture Development during Grain Growth in Nonoriented Electrical Steels
Bibliographic record
Abstract
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}͗112͘ components, are weakened during grain growth.In the 1 % Si specimens, the main texture components, {111}͗112͘ and {111}͗110͘, 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".