Multi-scale Analysis of Electromagnetic Energy Input using Swarm: Quantifying Key Scales in Magnetosphere-Ionosphere Coupling
Bibliographic record
Abstract
Electromagnetic energy transfer in magnetosphere-ionosphere coupling (MIC) is an inherently multiscale process, where the relative contributions of various scale sizes, linked to various auroral phenomena, are largely unknown. While work in previous decades has largely focused on large scales, in recent years with the development of new instrumentation smaller scale electromagnetic disturbances have once again come into focus. Recent work by the authors has demonstrated evidence that small-scale processes appear to be so important as to potentially account for a global interhemispheric asymmetry in ionospheric energy input. This study attempts to statistically quantify the contribution of energy at the small and mesoscales using Poynting flux, calculated using the unprecedented ESA Swarm mission dataset of simultaneous electric and magnetic field measurements at 16 Hz, with statistics now spanning several years. We find important contributions at small scales to the total energy budget, while at the same time noting that there appears to be a limit above which energy content tends to drop off. In the context of previous observations from other spacecraft this may shed light on key small-scale processes happening in and around the auroral acceleration region, in particular discrete arcs and Alfvén wave reflection from the ionosphere, which are important in forming inputs to coupled magnetosphere-ionosphere-thermosphere modelling studies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 | 0.000 |
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".