Response of low-latitude ionosphere to 24 April 2023 geomagnetic storm over the American, African, and Asian–Australian sectors
Bibliographic record
Abstract
The responses of the low-latitude ionospheres to the April 2023 geomagnetic storm were investigated using observations from the Global Positioning System-Total Electron Content (GPS-TEC) over the American, African, and Asian–Australian sectors. The TEC distribution from the GPS-TEC receivers across the latitudes was utilized to delineate equatorial ionization anomaly (EIA) structures across the regions. During the main phase of the storm, American EIA crests shifted equatorward with a significant reduction in TEC magnitude (negative ionospheric disturbance), while African crests remained unchanged compared to the quiet period, accompanied with a slight enhancement in TEC magnitude (positive ionospheric disturbance). Conversely, the Asian–Australian EIA crests collapsed to form an extended southern crest with significant enhancement in the TEC magnitude (positive ionospheric disturbance). These longitudinal differences across the sectors depended on couples of physical processes such as storm-time prompt penetration electric field orientation, local storm-time occurrences, variation in inferred E × B drift, storm-time neutral wind, and variations in air composition. However, the geomagnetic storm overall effects on the American sector were ∼3% and ∼7% to the northern and southern crests, respectively. African sector observed ∼6% and 5% toward the northern and southern EIA crests, respectively, while the Asian–Australian sector, witnessed ∼32% effect to the southern EIA crest.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".