3D magnetic modelling and inversion incorporating self-demagnetisation and interactions
Bibliographic record
Abstract
Self-demagnetisation can significantly reduce the amplitude and modify the shape of the magnetic response from highly magnetic bodies. For quasi-planar bodies, only the transverse component of magnetisation is reduced, with the result that the direction of magnetisation rotates towards the plane of the body. Furthermore, when highly magnetic bodies are in close proximity, the assumption of uniform inducing field is violated. Rather, highly magnetic bodies can modify the local magnetic field appreciably, with the result that the magnetisation induced in one body is affected by the magnetisations induced in all the others. It is important to take such interactions between highly magnetic bodies into account.Potential field modelling and inversion software “VPmg” has been upgraded to account for self demagnetisation and interaction between magnetic bodies. The algorithm computes H-field perturbations at the model cell centres in two stages: initialisation and optimisation. During initialisation, a demagnetisation tensor is estimated for each cell, from which a first estimate for the H-field perturbation is derived. During optimisation, the H-field field estimate is refined iteratively via an inversion procedure. Remanence can be taken into account.The algorithm has been validated for homogeneous spheres, spheroids, slabs, and cylinders. It has also reproduced magnetic interactions between two horizontal cylinders for the case published by Hjelt (1973). Explicit verification for complex heterogeneous bodies requires a suitable independent algorithm for benchmarking.The application to inversion in highly magnetic environments is illustrated on field data examples.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".