Multi‐Event Analysis of Magnetosphere‐Ionosphere Coupling of Nighttime Medium‐Scale Traveling Ionospheric Disturbances From the Ground and the Arase Satellite
Bibliographic record
Abstract
Abstract Kawai et al. (2021) reported the first ground‐satellite conjugate observation of nighttime medium‐scale traveling ionospheric disturbances (MSTIDs), by analyzing measurements from an airglow imager at Gakona (geographic latitude: 62.39°N, geographic longitude: 214.78°E, magnetic latitude: 63.60°N) and the Arase satellite in the magnetosphere on 3 November 2018. The Arase satellite observed variations in both the polarization electric field and the electron density as the Arase footprint passed through the MSTID structures in the ionosphere. In this study, we investigated whether these electric field and density variations associated with MSTIDs at subauroral latitudes are always observed by Arase in the magnetosphere. We used three airglow imagers installed at Gakona, Athabasca (geographic latitude: 54.60°N, geographic longitude: 246.36°E, magnetic latitude: 61.10°N), and Kapuskasing (geographic latitude: 49.39°N, geographic longitude: 277.81°E, magnetic latitude: 58.70°N) and the Arase satellite. We found eight observations of MSTIDs conjugate with Arase. They indicate that electric field and density variations associated with MSTIDs are not always observed in the magnetosphere. These variations tend to be observed in the magnetosphere during geomagnetically quiet times and when the amplitude of the MSTID is large. We categorized the MSTIDs into those caused by plasma instabilities and gravity waves and found that the electric field and density variations can be observed in the magnetosphere for both types of MSTIDs.
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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 | 0.001 | 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".