Stratified multifractal magnetization and surface geomagnetic fields-I. Spectral analysis and modelling
Bibliographic record
Abstract
Scaling models of geofields attempt to capture the strong and wide‐range variability ubiquitous in geosystems. Unfortunately, they are generally both isotropic (self‐similar) and monofractal (non‐intermittent, quasi‐Gaussian). In this first paper of a two‐paper series, we lift the first of these restrictions, arguing that anisotropic scaling is essential for taking into account the stratification of the Earth and its consequences. In particular, at horizontal scales below several thousand kilometres we model the thin or Curie‐depth‐limited crustal magnetization and the corresponding surface magnetic field (B) by using anisotropic scaling. We show that it generically gives rise to a new intermediate scaling (‘red noise’) surface B field regime quantitatively very close to that observed on two sets of regional surface B field surveys. This scaling is impossible to explain using standard self‐similar models. Using these data as well as horizontal and vertical susceptibility data, we estimate the basic model parameters and show that that model is compatible with the available data. In Paper II we lift the monofractal restriction and perform multifractal analyses; we then extend the anisotropic scaling model to include multifractal B and magnetization fields.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".