Extreme Value Analysis of Induced Geoelectric Field in South Africa
Bibliographic record
Abstract
Abstract Extreme geomagnetic disturbances occur rarely but can have great impact on technological systems such as power supply networks. Long‐term planning for extreme events requires the estimation of event impact for occurrence periods greater than the length of observed data. With this in mind an analysis of extreme geomagnetic events observed in South Africa (middle geomagnetic latitude) is performed over four solar cycles (1974–2015). An algorithm to identify active periods with minimumSYM‐H ≤−100 nT is demonstrated. The sum of induced electric field over the course of each event is used to characterize the severity of each active period. It is found that the severity index (accumulated electric field magnitudeΣE) shares a highly linear relationship with accumulatedSYM‐Hover each event. The index ΣEis lognormal distributed, with tail deviating greater than lognormal, confirming heavy‐tailed occurrence. A general Pareto distribution is fitted to the tail of the distribution and extrapolated to calculate the return levels of extreme events. Return levels of once in 100 and once in 200 year events are estimated to be 9.4 × 104 mV/km min and 1.09 × 105 mV/km min, respectively. The top three events, in ascending order of severity, are the March 1989 storm, the events of late October 2003, and the April 1994 event—a long interval of coronal‐hole driven disturbances, bookended by two intense geomagnetic storms.
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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".