First-order-reversal-curve (FORC) diagrams for pseudo-single-domain magnetites at high temperature
Bibliographic record
Abstract
Abstract The recently developed first-order reversal curve (FORC) technique for rapidly examining magnetic domain state has great potential for paleomagnetic and environmental magnetic investigations. However, there are still some gaps in the basic understanding of FORC diagrams, in particular the behavior of pseudo-single-domain (PSD) grains and the contribution of magnetostatic interactions. In this paper we address some of these problems. We report the first FORC diagrams measurements on narrowly sized and well-characterized synthetic PSD through multidomain (MD) magnetite samples. The FORC diagrams evolve with grain size from single-domain (SD)-like to MD-like through the PSD grain size range. Since each sample contains grains of essentially a single size, individual PSD grains evidently contain contributions from both SD-like and MD-like magnetic moments, in proportions that vary with grain size; the evolving FORC diagrams cannot be due to physical mixtures of SD and MD grains of widely different sizes. The FORC diagrams were all asymmetric. Small PSD samples have FORC diagrams with a distinctive closed-contour structure. The distributions of the larger MD grains display no peak, and lie closer to the interaction-field axis. To assess the effect of magnetostatic interactions, we measured FORC diagrams between room temperature and the Curie temperature. On heating the FORC distributions contract without changing shape until ∼500°C. Above this temperature the diagrams become more MD-like, and in addition become more symmetric. The temperature dependence of the interaction-field parameter is proportional to that of the saturation magnetization, in accordance with Neel’s interpretation of the Preisach diagram. The decrease in asymmetry with heating suggests that the origin of the asymmetry lies in magnetostatic interactions. The magnetic hysteresis parameters as a function of temperature were determined from the FORC curves. As the grain size decreased the normalized coercive force was found to decrease more rapidly with temperature.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".