An iterative method to obtain non-uniform field distribution in magnetic substrates
Bibliographic record
Abstract
An iterative method to find the non-uniform field distribution for arbitrary shapes is studied. In this method, when a magnetic material is placed in an applied field H/sub a/ produced by an external source, interaction occurs between the applied field and the magnetic material. Initially, with no demagnetizing field, the internal field equals the applied field until the material becomes saturated in the direction of the local applied field when the field is sufficiently strong. The main iteration loop starts with this initial value for the magnetization M. The material produces a contribution, a demagnetizing field H/sub d/, to the magnetic field in which when added to the applied magnetic field, results in the total internal field H/sub i/. The magnetic material now responds to this new total field and the magnetization redistributes itself in the new direction of the local total field. The iteration continues with this new magnetization, producing new demagnetizing and total internal fields. Results show that after further iterations the field becomes more parallel and the distribution of H/sub i/ is found to be symmetric with respect to the corners. The flux originates in the upper half and ends in the lower half following the general direction of H/sub a/.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".