Bias-field dependence of the spatiotemporal evolution of magnetization reversal in a mesoscopic<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mrow><mml:msub><mml:mrow><mml:mi mathvariant="normal">Ni</mml:mi></mml:mrow><mml:mrow><mml:mn>80</mml:mn></mml:mrow></mml:msub></mml:mrow><mml:mrow><mml:msub><mml:mrow><mml:mi mathvariant="normal">Fe</mml:mi></mml:mrow><mml:mrow><mml:mn>20</mml:mn></mml:mrow></mml:msub></mml:mrow></mml:math>element
Bibliographic record
Abstract
Ultrafast magnetization reversal dynamics in a ${\mathrm{Ni}}_{80}{\mathrm{Fe}}_{20}$ microstructure (10 \ensuremath{\mu}m by 2 \ensuremath{\mu}m in size and 15 nm thick) is studied using time-resolved scanning Kerr microscopy. The temporal evolution of the magnetization reversal reveals a dramatic reduction in switching time, when a steady transverse biasing field accompanies the pulsed longitudinal switching field applied to the sample. According to the analysis of time-domain images, it is concluded that the abrupt change of the switching time is due to a change in the magnetization reversal mode: the nucleation dominant reversal process is replaced by quasicoherent domain wall motion in the presence of an additional transverse biasing field. The experimental data are compared to results from micromagnetic modeling, based on the Landau-Liftshitz-Gilbert equation. The observed distinct magnetization reversal behaviors dependent on applied field conditions are well reproduced in the simulations.
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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) | 0.003 | 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".