Influence of Solar Rotation Influence on Ionospheric/Thermospheric Parameters: Modeling and Observations for Case Studies
Bibliographic record
Abstract
We investigated the effect of ~27 day solar rotation on the thermosphere-ionosphere system at different latitudes using both model results and multi-instrumental observation data. Considered ionospheric stations (ionosondes, GPS receivers, ISR radars) were located from the middle to high latitudes in Northern hemisphere. We analyzed also TIMED/GUVI variations of the O/N2 ratio in the thermosphere. Three different temporal periods were considered: December 2012-January 2013; January 2014; June-July 2014. The Global Self-consistent Model of the Thermosphere, Ionosphere, and Protonosphere (GSM TIP) were used for interpretation of coupled processes in the thermosphere-ionosphere system during one solar rotation cycle. There is a distinct response of daytime ionospheric electron density to the ~27-day variation in solar flux (F10.7). Using comparative and correlative analysis we revealed a delay in variation of modeled daytime critical frequency (foF2) and total electron content (TEC) with respect to F10.7 variation. According to model results variations in O/N2 ratio seems to be the main possible mechanism for this delay. Some model/data disagreement was discussed in context of importance of atmosphere-ionosphere coupling and geomagnetic control of ionospheric variability. Seasonal changes and difference between ~27-day variation in foF2 and TEC were discussed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 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".