Monitoring Global Ionospheric Conditions With Electromagnetic Lightning Impulses Registered in Extremely Low Frequency Measurements
Bibliographic record
Abstract
The Extremely Low Frequency band (ELF: 0.03 – 1000 Hz) electromagnetic signals from thunderstorm lightning discharges can propagate around the globe in the Earth-ionosphere resonance cavity and thus be used for ionosphere monitoring. We use ELF observations of the World Wide Lightning Location Network (WWLLN) impulses to examine ELF propagation velocity and arrival azimuth under diurnal changes in two days of September 20th and 21st, 2023. Also temporary effects of solar flares’ ionizing fluxes are monitored, leading to increasing the ELF signal propagation speed modulated by the X-ray flux intensity. We present for the first time a simple method for automatic and large-scale analysis, utilizing data from two registration systems (ELF and WWLLN) and enabling easy evaluation of changes in wave propagation speed. The compared samples of WWLLN impulses generated in selected azimuth and distance sectors for Africa and America reveal varying effects of signal refraction, with increased azimuth changes for signals propagating across the ionospheric ionization gradients. The method has a potential to become a standard tool for the analysis and monitoring of the lower layers of the ionosphere.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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".