The 27-Day Oscillation in Ionospheric Total Electron Content Observed by GNSS
Bibliographic record
Abstract
The 27-day oscillation in total electron content (TEC) is analysed by means of world maps of TEC. The TEC maps are derived from measurements of the ground receiver network of the Global Navigation Satellite System (GNSS) and are provided by the International GNSS Service (IGS). The observed 27-day oscillation in TEC is mainly due to the 27-day solar rotation period, which induces a 27-day oscillation in extreme ultraviolet radiation (EUV) of the Sun. Analysing the time interval from 2003 to 2020, cross-correlation of the 27-day oscillation of the solar MgII-index of the Solar Radiation and Climate Experiment (SORCE) and the 27-day oscillation in TEC shows an average time delay of about 1.1 days for the ionospheric response with respect to the solar EUV variation. The average correlation coefficient of the solar and the ionospheric variation is 0.85. The cross-correlation of the 27-day oscillation in solar radio flux F10.7 and the 27-day oscillation in TEC gives a time lag of about 1.3 days and an average correlation coefficient of 0.78. The world maps of the amplitude of the 27-day oscillation in TEC are discussed for the TEC data from 1998 to 2024. Finally, TEC composites are derived for F10.7 enhancement events and geomagnetic storms.
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 | 0.000 |
| 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) | 0.001 | 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".