Experimental comparison of FORC and remanent Preisach diagrams
Bibliographic record
Abstract
First-order reversal curve (FORC) and remanence-based Preisach diagrams are alternative ways of determining the Preisach distribution of a sample, which incorporates information about the coercivity spectrum and the distribution of interactions and self-demagnetizing fields. We compare results of the two methods for well-characterized synthetic and natural samples containing single-domain (SD) and pseudo-SD (PSD) magnetite, maghemite, titanomagnetite and titanomaghemite. The greater time requirements of remanence as opposed to in-field measurements limited our Preisach diagrams to a few hundred points, compared to several thousand points for the corresponding FORC diagrams. Only minimal smoothing could be applied in order to limit the regions near the axes of the diagrams in which function values must be extrapolated. In spite of these restrictions, we find excellent agreement between the essential features of the distributions determined by the two methods. The main features, the location and spreading of the distribution peak, are very consistent. However, the low-coercivity part of the Preisach distribution is sometimes poorly resolved or not imaged at all for remanence-only measurements. Features in this region can be diagnostic of PSD and multidomain (MD) grains. The essential agreement between our FORC and Preisach diagrams in the region where they overlap justifies using the much faster FORC routine instead of traditional remanence-based Preisach methods to determine the Preisach distribution of palaeomagnetic samples without strong interactions. We propose a symmetric FORC protocol that would permit separation of the irreversible and reversible parts of the Preisach distribution. The irreversible part is what is determined by remanence-only methods and what is desired for characterization of the remanence behaviour of palaeomagnetic samples. The reversible part is most significant in detecting MD behaviour and screening out samples containing large PSD and MD grains.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".