GPS TEC variations in the polar cap ionosphere: Solar wind and IMF dependence
Bibliographic record
Abstract
Abstract This statistical study examines the solar wind dependence of total electron content (TEC) variations arising from mesoscale (tens to hundreds of kilometers) structuring of the polar cap ionosphere. Six years of TEC measurements were collected from five high‐data rate Global Positioning System (GPS) receivers of the Canadian High Arctic Ionospheric Network (CHAIN), from which high‐resolution magnetic local time‐latitude maps of TEC variation occurrence rate and amplitude were created. Ionosonde radars were used to identify TEC variations arising from ionization of the E and F region ionospheres. Statistical TEC maps were examined as a function of solar wind and interplanetary magnetic field (IMF) measurements. Statistical results showed that occurrence rate of TEC variations was highest in localized dayside regions, with exact local time and latitude of peak occurrence depending primarily on the dayside coupling rate of the solar wind and magnetosphere, as well as IMF orientation and magnitude in the Y‐Z plane. Occurrence of TEC variations throughout the polar cap increased with solar wind‐magnetosphere coupling rate and IMF magnitude. The solar wind dependence of occurrence rate largely reflected the location and rate of dayside magnetic reconnection and subsequent particle precipitation and polar cap convection. Amplitudes of TEC variations were largest around noon and increased throughout the polar cap with increased solar wind‐magnetosphere coupling rate. These statistical results improve upon the existing observational picture of the polar ionosphere and will potentially facilitate development of models and techniques for mitigating impacts of the polar ionosphere on navigation signals and communication links.
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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 | 0.002 |
| 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) | 0.001 | 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".