Ultra Low Frequency Modulation of Energetic Electron Precipitation in the D-Region Ionosphere in a Magnetically Quiet Time Using OCTAVE Very Low Frequency and Low Frequency (VLF/LF) Observations
Bibliographic record
Abstract
There are several studies of energetic electron precipitation (EEP) modulated by ultra low frequency (ULF) during substorms, although there are few in geomagnetically quiet times.At present, it is not clear how often such quiet time EEP occurs and the mechanism involved.In this study, we investigate EEP modulated by ULF (,5 Hz) from Observation of CondiTion of ionized Atmosphere by VLF Experiment (OCTAVE) very low frequency (VLF; 3 kHz to 30 kHz) and low frequency (LF; 30 kHz to 300 kHz) observations in geomagnetically quiet times.The OCTAVE is a worldwide network of VLF/LF signals that we established.The VLF/LF transmitter signals from four transmitters (NLK, NDK, WWVB, and NAA) in the USA were received at Athabasca (ATH; 54.7 8 N, 113.3 8 E, L ¼ 4.3), Canada.There were oscillations in amplitude on the NDK-ATH and NLK-ATH paths with periods of 240 s and 270 s during magnetically quiet times from 11:20 Universal Time (UT) to 11:40 UT on October 9, 2017, respectively.The amplitudes of the VLF/LF variations were 3 dB and 1 dB on the NDK-ATH and NLK-ATH paths, respectively.Based on the wave-hop method, reflection height varied by ~4.3 km during the VLF/LF oscillations, indicating that the electron density in the D-region ionosphere increased by ~680 cm À3 (110%) at a 91 km height.The H component of ground-based magnetic field data at ATH and low latitudes showed the geomagnetic Pi2 pulsations with a similar period to that in the VLF/LF signals.The main cause of the VLF/LF oscillations could be ULF-modulated EEP due to the lowering of mirror points.
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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 | 0.000 |
| 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) | 0.000 | 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".