Using Mutual Information to Determine Geoeffectiveness of Solar Wind Phase Fronts With Different Front Orientations
Bibliographic record
Abstract
Abstract The geoeffectiveness of solar wind shocks depends on angle with respect to the Sun‐Earth line, with highly angled solar wind shocks being less geoeffective than nearly frontal solar wind shocks. However, it is unclear whether this holds for the orientation of structures in nonshocked solar wind. In this paper, we perform a mutual information analysis of 18 years of in situ solar wind and ground magnetometer data in order to investigate the effects of solar wind phase front orientation on solar wind geoeffectiveness (indicated by SuperMAG SME, the SuperMAG enhanced version of the AE index). Since geomagnetic response is strongly influenced by interplanetary magnetic field (IMF) Bz, and IMF Bz affects phase front orientation, we use conditional mutual information to account for the effect of Bz on geomagnetic activity. In contrast to what has been found for solar wind shocks, we find that during times of IMF Bz > 0, phase fronts aligned with the average Parker spiral direction (45° azimuth, 0° inclination) tend to be associated with higher geomagnetic activity (SME > 500 nT) than would be expected if IMF Bz and phase front orientation quantities were unrelated. During times of IMF Bz < 0, there is no connection between solar wind phase front orientation and geomagnetic activity (SME). We believe that Parker spiral‐aligned phase fronts being associated with higher geomagnetic activity during times of IMF Bz > 0 is due to constant phase front orientation allowing for more efficient energy transfer either through viscous interaction or high‐latitude reconnection.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".