A review of the theory of 3‐D Alfvén (field line) resonances
Bibliographic record
Abstract
This review article aims to summarise recent developments in Alfvén resonance theory, with a focus on applications to magnetospheric ultra-low frequency (ULF) waves, though many of the ideas are relevant for applications in other fields as well. The key aspect we treat is how Alfvén resonance manifests in a fully 3-D varying medium. The prerequisite ideas are developed in a reasonably comprehensive introduction, which would be a good starting point for any interested reader looking to gain an understanding of the Alfvén resonance process, as well as where to find associated reading. The main part of the review is split into three sections. We firstly consider results from numerical simulations of relatively simple magnetic field geometries, such as 2-D and 3-D dipoles, to develop the fundamental properties of 3-D Alfvén resonances. Secondly, we review previous simulations in more general magnetic field geometries, reconciling these results with those from the simpler dipole cases. Thirdly, in light of these numerical results, we review theoretical studies using various analytical methods to find approximate solutions to the pertinent magnetohydrodynamic (MHD) equations. The review is concluded with a discussion of these different approaches, as well as linking these ideas to their importance for observations. Finally, we discuss potential future developments in this research area.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".