Statistical Analysis of Ultra‐Low‐Frequency Total Electron Content Disturbances: Relationship to Magnetospheric Waves
Bibliographic record
Abstract
Abstract Disturbances in ionospheric Total Electron Content (dTEC) with frequencies of 1–100 mHz can be driven from above by processes in the magnetosphere and below by processes on the Earth's surface and lower atmosphere. Past studies showed the potential of dTEC as a diagnostic of magnetospheric Ultra Low Frequency (ULF) wave activity and demonstrated that ULF dTEC can impact space weather by, for example, changing ionospheric conductance. However, most past work has focused on single event studies, lacked magnetospheric context, or used sampling rates too low to capture most ULF waves. Here, we perform a statistical study using Time History of Events and Macrsoscale Interactions during Substorms (THEMIS) satellite conjunctions with a ground‐based magnetometer and Global Navigation Satellite System (GNSS) receiver at 65° magnetic latitude. We find that magnetospheric ULF waves generate dTEC variations across the broad range of frequencies examined in this study (2–50 mHz), and that ULF dTEC wave power is correlated with Kp, AE, solar wind speed, and magnetic field wave power observed in the magnetosphere and on the ground. We further find that magnetospheric ULF waves generate dTEC amplitudes up to TECU ( background), with the largest amplitudes occurring during geomagnetically active conditions, at frequencies below 7 mHz, and at local times near midnight. We finally discuss the implications of our results for magnetosphere‐ionosphere coupling and remote sensing techniques related to ULF waves.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".