Different magnetospheric modes: solar wind driving and coupling efficiency
Bibliographic record
Abstract
Abstract. This study describes a systematic statistical comparison of isolated non-storm substorms, steady magnetospheric convection (SMC) intervals and sawtooth events. The number of events is approximately the same in each group and the data are taken from about the same years to avoid biasing by different solar cycle phase. The very same superposed epoch analysis is performed for each event group to show the characteristics of ground-based indices (AL, PCN, PC potential), particle injection at the geostationary orbit and the solar wind and IMF parameters. We show that the monthly occurrence of sawtooth events and isolated non-stormtime substorms closely follows maxima of the geomagnetic activity at (or close to) the equinoxes. The most strongly solar wind driven event type, sawtooth events, is the least efficient in coupling the solar wind energy to the auroral ionosphere, while SMC periods are associated with the highest coupling ratio (AL/EY). Furthermore, solar wind speed seems to play a key role in determining the type of activity in the magnetosphere. Slow solar wind is capable of maintaining steady convection. During fast solar wind streams the magnetosphere responds with loading–unloading cycles, represented by substorms during moderately active conditions and sawtooth events (or other storm-time activations) during geomagnetically active 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 | 0.000 |
| 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".