The Study of ionospheric TEC variation during a geomagnetic storm over low and high-latitude regions
Bibliographic record
Abstract
The present study shows the ionospheric response during an intense geomagnetic storm on 22-27 June 2015 with minimum disturbance storm time (dst) index -198 nT in terms of Global Positioning System (GPS) derived Vertical Total Electron Content (VTEC) variation along two different longitudes and latitudes.Two stations: IISC, Bangalore, India (13.021 0 N, 77.570 0 E) and DGAR, Diego Garcia Island, United Kingdom (7.270 0 S, 72.370 0 E) are taken along the 75 0 E longitude whereas, CORD, Cordoba, Argentina (31.528 0 S, 64.470 0 W) and QIKI, Qikiqtarjuaq (Baffin Island), Canada (67.559 0 N, 64.034 0 W) are taken along the 64 0 W longitude for our study.The changes in ionospheric TEC during the occurrence of storms compared to quiet conditions are also shown here.The difference (dTEC) between the storm days VTEC and quiet mean VTEC is also calculated to observe the positive and negative storm effects on TEC.Characteristics of various geomagnetic indices related to the storm (dst index, Bz, Ey) are observed during the storm period.The results show the significant positive and negative storm effects over the equatorial and low latitude regions in comparison to the high latitude region.The anomalous behavior of the TEC variation during the geomagnetic storm over the low equatorial stations is related to various peculiar phenomena observed over the ionosphere in the equatorial and low latitude regions such as the fountain effect, EEJ, etc.
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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".