Properties of <i>AE</i> indices derived from real‐time global simulation and their implications for solar wind‐magnetosphere coupling
Bibliographic record
Abstract
Real‐time magnetohydrodynamic (MHD) simulation of the solar wind‐magnetosphere‐ionosphere (S‐M‐I) coupling system was used to calculate auroral electrojet (AE) indices. This simulation reproduces the magnetic field configurations in the magnetosphere, magnetospheric convection, and field‐aligned currents (FACs) using the upstream boundary conditions with the interplanetary magnetic field (IMF), solar wind speed, temperature, and proton number density measured by the ACE spacecraft. The electrical potential at 3 RE (Earth radii) from the center of the Earth is mapped on the ionosphere. The ionospheric currents are deduced from Ohm's law to match the divergence of Pedersen and Hall currents from FACs. The AE indices are obtained from the magnetic field perturbation caused by the simulated ionospheric currents. We compared the simulated AE indices for 247 d with the AE indices deduced from the magnetic variations at up to 12 stations located around the auroral latitude. The results show that the simulated AE reproduces the observed AE indices well. Of the 247 d, 64% had cross‐correlation coefficients of more than 0.5. We also found that the simulated AE indices do not correlate well with the observed AE indices when the standard deviations of variations in the observed AE indices are less than 100 nT. When variations in the AE indices are small, some of the short‐period perturbations of the electromagnetic energy flowing from the solar wind into the magnetosphere is absorbed or filtered in the real S‐M‐I coupling system by some mechanism that is not included in our MHD simulation and that the resulting fluctuation in the AE indices is damped compared with the simulation.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".