Quantifying the Effects of Solar Wind Fluctuations on the Solar Wind‐Magnetosphere Interaction
Bibliographic record
Abstract
Abstract Ala‐Lahti et al. (2024, https://doi.org/10.1029/2024GL112922 ) present results from a global magnetohydrodynamic simulation of a single geomagnetic substorm for four scenarios: the original solar wind conditions, smoothed low‐frequency solar wind conditions, constant solar wind conditions with a boxcar averaged north/south component of the interplanetary magnetic field (IMF), and the boxcar‐averaged scenario with ultra‐low‐frequency (ULF) fluctuations. Smoothed (<1 mHz) solar wind parameters capture the bulk of the interaction, boxcar averaging reduces the energy flow through the system by 15%–40%, and ULF fluctuations (2–8 mHz) only enhance interactions by 5%–15%. From this, we conclude that low‐frequency plasma and magnetic field variations dominate the interaction. Further global simulations and observational studies of different events will be needed to determine the significance of intrinsic magnetopause and magnetotail instabilities (rather than directly driven interplanetary magnetic field fluctuations). They will also be needed to generalize these results for the full range of solar wind and geomagnetic conditions.
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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 | 0.001 |
| 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".