ULF waves and their influence on radiation belt dynamics in Earth's magnetosphere
Bibliographic record
Abstract
Recent observations from the Van Allen Probes mission have established that Pc3-5 ultra-low-frequency (ULF) waves can energize ions and electrons via drift-resonance and drift-bounce resonance. The extent to which these waves contribute to the space weather of the belts is relatively poorly understood and requires sophisticated modelling and characterization of the dominant wave modes that arise in the development and recovery phase of geomagnetic storms. Despite more than four decades of observations and theoretical analysis of ULF waves, there is no framework for accurately assessing the global distribution of ULF waves and their influence on the ring current. In this presentation, we describe a new global model of ULF waves that incorporates non-dipolar geomagnetic fields. The model is constrained using the GCPM of cold plasma density model and a specification of the ionosphere using the IRI and MSIS models. An algorithm is applied to adjust the initial plasma state to a quasi-static equilibrium that is then driven by a global convection electric field and ULF wave source. For specific observations by the Van Allen Probes and ARASE mission, the effect of these ULF waves on radiation belt ions and electrons is evaluated utilizing test-particle methodology and Liouville's theorem, which enables the phase space density to be followed and compared one-for-one with the satellite observations.
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.
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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".