Ionospheric Density Variations Observed by the Radio Receiver Instrument on e‐POP/Swarm‐E
Bibliographic record
Abstract
Abstract Ionospheric density variations can be inferred by studying the effects of electron density structures on transionospheric High Frequency (HF) radio wave propagation. The Radio Receiver Instrument (RRI) on the Enhanced Polar Outflow Probe (e‐POP)/Swarm‐E is used to detect HF radio waves during transionospheric experiments conducted between the space‐based RRI and ground‐based HF transmitters. A Faraday rotation rate‐based method is used to convert RRI HF observations into ionospheric density variations. Two geomagnetically quiet‐day periods in December 2017 are examined, where HF waves from the Ottawa transmitter reveal ionospheric structures with scale sizes from 7 to 750 km. Comparisons with GPS differential Total Electron Content (dTEC) show similar scale sizes. While RRI dTEC agrees with GPS‐derived dTEC for large‐scale features, RRI additionally detects small‐scale fluctuations that are comparable to or surpass the magnitudes of large‐scale variations. Excursions from large‐scale variations observed by RRI are 2 TECU in narrow latitudinal bands of 0.25° corresponding to 25 km. RRI and GPS dTEC variations suggest the continuous existence of 300–350 km scale‐size structures on both days. Moreover the high sampling rate of RRI enhances the measurement capability of small‐scale spatial variations and indicates a quiet‐time ionosphere dominated by small‐scale total electron content variations. RRI measurements give insight into the scale size of localized, transient ionospheric phenomena that affect HF radio wave propagation.
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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 | 0.000 |
| 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".