The Tsallis statistical distribution applied to geomagnetically induced currents
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 Geomagnetically induced currents (GICs) have been long recognized as a ground effect arising from a chain of space weather events. GICs have been measured and modeled in many countries, resulting in a considerable amount of data. Previous statistical analyses have proposed various types of distribution functions to fit long‐term GICs data sets. However, these extensive statistical approaches have been only partially successful in fitting the data sets. Here we use modeled GICs data sets calculated in four countries (Brazil, South Africa, United Kingdom, and Finland) using data from solar cycle 23 to show a plausible function based on a nonextensive statistical model of the q ‐exponential Tsallis function. The fitted q ‐exponential parameter is approximately the same for all locations, and the Lilliefors test shows good agreement with the q ‐exponential fits. From this fit, we compute that the likely numbers of extreme GICs events over the next ten solar cycles are 1–2 for both Finland and United Kingdom, at least one for Brazil and less than one event for South Africa. Our results indicate that the nonextensive statistics are a general characteristic of GICs, suggesting that the ground current intensity has a strong temporal correlation and long‐range interaction.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 0.002 |
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