UNCLASSIFIED/UNLIMITED UNCLASSIFIED/UNLIMITED VLF Phase Perturbations Produced by the Variability in Large (V/m) Mesospheric Electric Fields in the 60 – 70 km Altitude Range
Bibliographic record
Abstract
The large (V/m) mesospheric electric fields have been identified as a possible cause of VLF phase perturbations. These fields affect the fundamental processes that govern the lower D region parameters, primarily the electron temperature and effective collision frequency. The main ionospheric parameter needed to calculate VLF phase perturbations is the low-frequency electron plasma conductivity. All the electric field data available to 1990 were collected with electric field sensors on board more than 50 rockets launched over approximately 30 years in the USSR and the U.S.A., which were insufficient to address VLF phase perturbations. This paper discusses the progress made in addressing large (V/m) mesospheric electric fields between 60- and 70-km altitudes since 1990. It focuses on achieving the breakthrough, the development of a radio wave technique for sensing large electric fields remotely by using MF radar, and on the fact that the electric field variability leads to the variability of ionospheric conduction contours by a few kilometers in altitude. The statistical analysis of the large mesospheric electric field data acquired in the 60- and 67-km altitude region in Canada and Ukraine suggests that large mesospheric electric fields may occur during about 70 % of all the time. However, reasonable assessments of VLF phase perturbations need information on the temporal and especially spatial
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.300 | 0.335 |
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".