Evidence for the Significant Differences in Response Times of Equatorial Ionization Anomaly Crest Corresponding to Plasma Fountains During Daytime and Post‐Sunset Hours
Bibliographic record
Abstract
Abstract Based on 10 years' (2010–2019) of vertical total electron content (VTEC) data from Ahmedabad (23.0°N, 72.6°E, dip angle 35.2°) and campaign based OI 630.0 nm airglow intensity measurements from Mt. Abu (24.6°N, 72.7°E, dip angle 38.0°), it is shown that plasma density over the equatorial ionization anomaly (EIA) crest region increases in varying degrees during post‐sunset hours (2000–2100 LT) in magnetically quiet periods. The post‐sunset peak in VTEC precedes the corresponding peak in airglow intensity. By comparing post‐sunset VTEC enhancements with ionosonde observations from Tirunelveli (8.7°N, 77.7°E, dip angle 1.7°), it is shown that pre‐reversal enhancement (PRE) of the zonal electric field causes these enhancements over the EIA crest region. These observations are supported by TEC measurements by GAGAN (GPS Aided Geo Augmented navigation), the Indian Satellite‐based Augmentation System (SBAS). Comparison of average VTEC variations with global empirical model drifts reveals that the post‐sunset enhancements in VTEC occurs ∼1.7 h after the PRE and are significant only during December solstice and equinoctial months in high solar activity years similar to seasonal variations in PRE amplitudes. This time delay (response time of EIA crest) is almost half compared to the average response time (3–4 h) associated with the daytime fountain. Based on the latitudinal gradient in SBAS‐TEC, it is proposed that the PRE drives plasma from 5°N to 10°N magnetic latitudes to the EIA crest region leading to shorter response time. These results show the important role of the PRE in conditioning the EIA crest region.
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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".