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Bibliographic record
Abstract
Remanent magnetisation is an important consideration in magnetic interpretation. In some cases failure to properly account for remanence can lead to completely erroneous interpretations. In general the strength and orientation of remanence are unknown. Two main strategies have been pursued for “unconstrained” inversion of large data sets. One strategy is to invert quantities, such as total magnetic gradient (3D analytic signal), which are insensitive to magnetisation direction. The inverted property is then magnetisation amplitude. Another strategy is to invert for the magnetisation vector, allowing its three components to vary freely. These approaches are useful, but the resulting magnetisation models are highly non-unique.When interpreting magnetic data in tandem with geological modelling there is greater potential to infer remanence parameters. Non-uniqueness is reduced if the shape of magnetic domains is constrained, especially if the susceptibility is known and if remanence can be assumed uniform. Accordingly, inverting for the remanent magnetisation of individual homogeneous geological units of arbitrary 3D shape is the subject of this paper. Our remanent magnetisation inversion (RMI) approach can be regarded as a generalisation of parametric inversion of simple geometric bodies.If susceptibility is known, the optimal remanent magnetisation vector within each selected unit is determined via iterative inversion. Sensitivity to change in magnetisation is determined in the x-, y-, and z-directions, and the perturbation vector is found via the method of steepest descent. If the susceptibility is unknown, the optimal susceptibility of each unit (subject to bounds) can be determined via a similar inversion procedure. The geological units can carry remanent magnetisation, but it is fixed during this stage. The susceptibility and/or remanence inversions can be repeated, if necessary, to refine the magnetic parameters. Self-demagnetisation and interactions are taken into account when susceptibilities are high.The application of the RMI algorithm is illustrated in examples for both known and unknown susceptibility.
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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) | 0.001 | 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 it