Satellite‐beacon Ionospheric‐scintillation Global Model of the upper Atmosphere (SIGMA) II: Inverse modeling with high‐latitude observations to deduce irregularity physics
Bibliographic record
Abstract
Abstract Ionospheric scintillation is caused by irregularities in the ionospheric electron density. The characterization of ionospheric irregularities is important to further our understanding of the underlying physics. Our goal is to characterize the intermediate (0.1–10 km) to medium (10–100 km) scale high‐latitude irregularities which are likely to produce these scintillations. In this paper, we characterize irregularities observed by Global Navigation Satellite System (GNSS) during a geomagnetically active period on 9 March 2012. For this purpose, along with the measurements, we are using the recently developed model: “Satellite‐beacon Ionospheric‐scintillation Global Model of the upper Atmosphere” (SIGMA). The model is particularly applicable at high latitudes as it accounts for the complicated geometry of the magnetic field lines in these regions and is presented in an earlier paper. We use an inverse modeling technique to derive irregularity parameters by comparing the high rate (50 Hz) GNSS observations to the modeled outputs. In this investigation, we consider experimental observations from both the northern and southern high latitudes. The results include predominance of phase scintillations compared to amplitude scintillations that imply the presence of larger‐scale irregularities of sizes above the Fresnel scale at GPS frequencies, and the spectral index ranges from 2.4 to 4.2 and the RMS number density ranges from 3e11 to 2.3e12 el/m 3 . The best fits we obtained from our inverse method that considers only weak scattering mostly agree with the observations. Finally, we suggest some improvements in order to facilitate the possibility of accomplishing a unique solution to such inverse problems.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".