Probability distribution of integrated power of equatorial ionosphere plasma density fluctuations measured by the Swarm Langmuir probes
Bibliographic record
Abstract
Ionospheric irregularities are structures or fluctuations of plasma density having different scale sizes. These irregularities can disrupt radio waves and produce errors in space-based or ground-based technologies, which depend on the GNSS/ GPS signals. Post-sunset ionospheric plasma irregularities are a common characteristic of the equatorial ionosphere. These irregularities, associated with plasma bubbles, are defined as strong density depletions relative to the background plasma as determined by in situ measurements. Finding a system parameter's probability distribution function (PDF) can lead us to understand the system's underlying physics. In this study, we investigate the probability distribution of the integrated power of post-sunset plasma density irregularities in the equatorial ionosphere measured with the Langmuir probes on Swarm C in four different frequency bands between 0-1 Hz for the entire Swarm mission. We find evidence of "heavy tail" distribution in the PDFs, indicating the system's complexity and self-organized criticality. Moreover, we study the relation between Integrated power and different geomagnetic indices, e.g. F10.7 and sunspot number, to find the potential drivers of severe events. While we find no obvious driver of individual events, we find a strong solar cycle dependence in their occurrence.
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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 | 0.002 |
| 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) | 0.001 | 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".