Comparative statistical analysis of storm time activations and sawtooth events
Bibliographic record
Abstract
Statistical properties of storm time magnetospheric activity are examined using superposed epoch analysis. We show that about half of storm time auroral electrojet activations have signatures that are typical of nonstorm substorms, including geostationary orbit injections and magnetic field dipolarizations. Analysis of a separate data set of sawtooth events shows that they have auroral and inner magnetosphere characteristics that are quite similar to those found generally during storm time activity. Hence it is concluded that the sawtooth events do not represent a specific class of magnetospheric activity. Examination of the solar wind and IMF properties showed that about 30% of storm time substorm‐like activations and about 20% of the sawtooth oscillations have associated solar wind or IMF triggers and that triggering is more likely during high solar wind pressure and fluctuating IMF. The solar wind‐magnetosphere coupling efficiency is shown to be independent of the solar wind Mach number or level of IMF fluctuations but dependent on the level of driving; when EY is small, the ionospheric dissipation, ring current intensification, and geostationary field stretching are relatively larger than when the driving EY is large.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".