Substorm occurrence rates, substorm recurrence times, and solar wind structure
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Two collections of substorms are created: 28,464 substorms identified with jumps in the SuperMAG AL index in the years 1979–2015 and 16,025 substorms identified with electron injections into geosynchronous orbit in the years 1989–2007. Substorm occurrence rates and substorm recurrence‐time distributions are examined as functions of the phase of the solar cycle, the season of the year, the Russell‐McPherron favorability, the type of solar wind plasma at Earth, the geomagnetic‐activity level, and as functions of various solar and solar wind properties. Three populations of substorm occurrences are seen: (1) quasiperiodically occurring substorms with recurrence times (waiting times) of 2–4 h, (2) randomly occurring substorms with recurrence times of about 6–15 h, and (3) long intervals wherein no substorms occur. A working model is suggested wherein (1) the period of periodic substorms is set by the magnetosphere with variations in the actual recurrence times caused by the need for a solar wind driving interval to occur, (2) the mesoscale structure of the solar wind magnetic field triggers the occurrence of the random substorms, and (3) the large‐scale structure of the solar wind plasma is responsible for the long intervals wherein no substorms occur. Statistically, the recurrence period of periodically occurring substorms is slightly shorter when the ram pressure of the solar wind is high, when the magnetic field strength of the solar wind is strong, when the Mach number of the solar wind is low, and when the polar‐cap potential saturation parameter is high.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it