Hysteresis loops revisited: An efficient method to analyze ferroic materials
Bibliographic record
Abstract
Hysteresis loops characterize a wide variety of behaviors in fields ranging from physics and chemistry to economics and sociology. In particular, they represent the main characteristic of ferroic materials such as ferromagnetic and ferroelectric, which, in recent years, have attracted much interest due to their multifunctional properties. Although measuring such loops may not be experimentally complicated, extracting the intrinsic values of the characteristic parameters of the loop may prove difficult due to the different contributions to the measured hysteresis. In this paper, a simple technique is proposed to analyze hysteresis loops and to extract solely the contribution of the ferromagnetic or ferroelectric material. Such method consists in differentiating the measured loop, deconvoluting the different contributions and selectively integrating only the signals belonging to the ferroic response. A discussion of the limitations of the method is presented. Different measured ferromagnetic and ferroelectric hysteresis loops were also used to validate the technique. Comparison between experimental and reconstructed data demonstrated the precision and reliability of the technique. Moreover, application of such method allowed us to highlight properties of a Bi2FeCrO6 room temperature multiferroic thin film that were not previously observed.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".