3D magnetic modelling and inversion incorporating self-demagnetisation and interactions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 it