Geomagnetically induced currents around the world during the 17 March 2015 storm
Bibliographic record
Abstract
Abstract Geomagnetically induced currents (GICs) represent a significant space weather issue for power grid and pipeline infrastructure, particularly during severe geomagnetic storms. In this study, magnetometer data collected from around the world are analyzed to investigate the GICs caused by the 2015 St. Patrick's Day storm. While significant GIC activity in the high‐latitude regions due to storm time substorm activity is shown for this event, enhanced GIC activity was also measured at two equatorial stations in the American and Southeast Asian sectors. This equatorial GIC activity is closely examined, and it is shown that it is present both during the arrival of the interplanetary shock at the storm sudden commencement (SSC) in Southeast Asia and during the main phase of the storm ∼10 h later in South America. The SSC caused magnetic field variations at the equator in Southeast Asia that were twice the magnitude of those observed only a few degrees to the north, strongly indicating that the equatorial electrojet (EEJ) played a significant role. The large equatorial magnetic field variations measured in South America are also examined, and the coincident solar wind data are used to investigate the causes of the sudden changes in the EEJ ∼10 h into the storm. From this analysis it is concluded that sudden magnetopause current increases due to increases in the solar wind dynamic pressure, and the sudden changes in the resultant magnetospheric and ionospheric current systems, are the primary drivers of equatorial GICs.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".