Solar Flux Dependence of Post‐Sunset Enhancement in Vertical Total Electron Content Over the Crest Region of Equatorial Ionization Anomaly
Bibliographic record
Abstract
Abstract Based on 10 years' of vertical total electron content (VTEC) data in solar cycle 24 from Ahmedabad (23.0°N, 72.6°E, dip angle 35.2°), a station under the crest region of equatorial ionization anomaly (EIA), it is shown that both the integrated residual and total post‐sunset enhancements in VTEC are conspicuous during Equinox and December solstice when solar flux level exceeds 110 solar flux unit (sfu) with the exception of the year 2012–2013. The post‐sunset enhancements are absent in June solstice at this local time even if the solar flux level exceeds 110 sfu. The integrated residual and total VTEC enhancements during post‐sunset hours are found to be linearly correlated with the solar flux level in Equinox and December solstice. It is noted that a parabolic fit works better for the integrated total VTEC enhancement during December solstice suggesting a possible saturation of VTEC enhancements at high solar flux levels. Based on these observations and Thermosphere Ionosphere Electrodynamics‐General circulation model (TIE‐GCM) outputs, it is argued that the pre‐reversal enhancement (PRE) in the equatorial F region zonal electric field works in tandem with latitudinal gradient in the F region plasma density to determine the degree of VTEC enhancement over the EIA crest region during post‐sunset hours. These results highlight the solar flux dependence of the post‐sunset enhancement of VTEC over the crest region and show that sudden stratospheric warming events in 2012–2013 suppressed these enhancements in December solstice even if solar flux levels exceeded 110 sfu.
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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".