Longitudinal dependence of ionospheric irregularities to maximum ring current and PPEF sensed by GNSS and magnetometers during the storm of 4 November 2021
Bibliographic record
Abstract
We employ multi-instrumental data to investigate the behavior of equatorial and low latitude ionosphere during the geomagnetic storm of November 3-6, 2021. We used TEC data obtained from GPS receiver stations located in the equatorial and low-latitudes of the Asian, African, and American sectors. It is found that the storm-time ionization level varies significantly in the trough and crest of EIA region over the three longitudes. ROTI is used to estimate the occurrence of ionospheric plasma irregularities during the storm. Usually, the main phase of the geomagnetic storm triggers the equatorial plasma irregularities and the recovery phase suppresses the occurrence of them. Here, we observed inhibition of the plasma irregularities over the three sectors during the main phase of the storm. We suspect this may be due to the injection of the PEFs which occur between local midnight and around noon during the main phase. The PEFs restrict the diffusion of plasma and therefore, suppress the occurrence of plasma irregularities during the main phase. During the recovery phase, moderate ionospheric irregularities occurred at local midnight in the American sector. In the African sector, the occurrence of weak irregularities can be seen before midnight on November 5 and 6. However, the Asian sector does not exhibit noticeable ionospheric irregularities during the storm. We conclude that the longitudinal variation in the development of ionospheric irregularities can be influenced by factors such as local time occurrence of maximum ring current, PPEF, disturbance wind dynamo electric field, and shielding electric field.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".