Spatiotemporal Development of Cosmic Noise Absorption at Subauroral Latitudes Using Multipoint Ground‐Based Riometers
Bibliographic record
Abstract
Abstract Electron density enhancements in the ionospheric D‐region due to the precipitation of high‐energy electrons (>30 keV) have been measured as increases in cosmic radio noise absorption (CNA) using ground‐based riometers. CNA has been studied since the 1960s. However, there have been few studies of the spatiotemporal development of CNA at multi‐point ground stations distributed in longitude at subauroral latitudes, where plasma particles with a wide energy range are intermingled. In this study, we analyzed the longitudinal development of CNA steep increases using simultaneous riometer observations at six stations at subauroral latitudes in Canada, Alaska, Russia, and Iceland over 3 years from 2017 to 2020. The results revealed that the occurrence rate of steep increases in CNA was highest at midnight at 22‐08 magnetic local time (MLT), and lowest near dusk at 17–21 MLT. We also showed statistically that the CNA steep increases expanded eastward on the dawn side and westward on the dusk side. The CNA expansion velocity was slightly faster than the results of previous studies in the auroral zone. Correlation and superposed epoch analyses of CNA with solar wind and geomagnetic parameters revealed that CNA intensity was dependent on the Interplanetary Magnetic Field Bz, Interplanetary Electric Field Ey, SYM‐H index, and SME index. These results indicate that the CNA at subauroral latitudes is closely related to solar wind and geomagnetic activities, and its propagation characteristics correspond to the dynamics of high energy electrons in the inner magnetosphere.
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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) | 0.000 | 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".