Interaction of plasma drifts: A hypothesis for equatorial spread F occurrence
Bibliographic record
Abstract
Studies on the generation mechanisms of post-sunset ionospheric irregularities, which constitute a threat to trans-ionospheric radio propagation, particularly at the African sector, are quite sparse. Recently, a GNSS scintillation monitor was co-located with an existing HF Doppler receiver at the University of Lagos (geographic: 3.27° E, 6.48° N; dip latitude −1.72°), Nigeria. The availability of data from both instruments for the period March–September 2022 provided an opportunity to study ionospheric irregularities using the datasets from the instruments. The post-sunset rise of the ionosphere was obtained from the HF Doppler monitor, while plasma bubble depth was determined from GPS total electron content (TEC) measurements. Ionospheric scintillation, determined from amplitude scintillation index (S4 index), was observed to peak in amplitude at latitudes 7°–10° south of the dip equator with a lower peak occurring in the vicinity of the dip equator. Amplified wave structures were consistently identified in filtered time series of slant TEC from GPS satellites in view of the receiver during the time of scintillation occurrence. These wave structures were predominant at about the time of local sunset, when shear flow between eastward and westward plasma drifts is known to be greatest. Rising plumes were also observed during local sunset, and the results suggest that these plumes could be signatures of interacting eastward and westward plasma drifts. Linear correlation analysis of S4 index and wave amplitude yielded a higher correlation index, compared with a similar analysis of S4 index and post-sunset rise. It is postulated that even when the post-sunset rise is low, the interaction of waves can generate rising plumes, which in turn facilitate the development of the Rayleigh-Taylor instability and ionospheric irregularities.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".