Case Studies on the Day‐to‐Day Variability in the Occurrence of Post‐Sunset Equatorial Spread F
Bibliographic record
Abstract
Abstract The occurrence of post‐sunset equatorial spread F (ESF) is well understood to be associated with the evening pre‐reversal enhancement (PRE). Earlier studies have shown that when large datasets are examined, there is a significant correlation between the occurrence of post‐sunset ESF and the evening PRE. However, this correlation is much lower when the events are examined on a day‐to‐day basis. This has led to the emergence of suggestions that the PRE may not be a necessary condition for the occurrence of post‐sunset ESF. This study presents the results of the occurrence of post‐sunset ESF using data obtained from the Lowell Digisonde at Ilorin, Nigeria (ILR; 4.68°E, 8.50°N; Dip latitude −1.25°), spanning May–September 2019. In this interval, data was available on 124 days and, of this number, post‐sunset ESF occurred on 99 days (79.8%). Of the 99 days for which post‐sunset ESF was observed, two events for which the evening PRE was absent were investigated in detail. Analysis of Digisonde data for these two selected events showed modulation of the bottomside F‐layer plasma in the evening sector by wave structures. Spectral analysis of Global Positioning System Total Electron Content from multiple ground stations across Nigeria showed the appearance of wave structures, consistent with standing waves, in the ionosphere. The results presented show that standing waves are a possible mechanism for the generation of post‐sunset ESF, even in the absence of the PRE.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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".