The Curvature of TEC as a Proxy for Ionospheric Amplitude Scintillation
Bibliographic record
Abstract
Abstract Fluctuations in the ionospheric electron density cause distortions in the Global Navigation Satellite Systems (GNSS) signals recorded on the ground. The examination of these distortions reveal some of the physical conditions under which the electron density fluctuations develop as well as their physical characteristics. Several studies have investigated the correlation between the rate of change of the total electron content and amplitude and phase scintillation indices and , respectively. These studies stipulate that could be used as a proxy for scintillation indices. The link between the scintillation indices and the variations in is investigated both theoretically and empirically. Our study shows that the second derivative (the Laplacian) of the provides a better diagnosis of the nature of the interaction of trans‐ionospheric radio signals with ionospheric irregularities. In the refractive case, the second derivative of fluctuations vanishes. In the diffractive limit, we show that the amplitude scintillation index and the standard deviation of the second derivative of are linearly dependent. The theoretical results are empirically validated with measurements of GNSS radio signals propagating through the auroral ionospheric region and recorded by ground receivers of the Canadian High Arctic Ionospheric Network (CHAIN). The present study suggests that the use of as a proxy for scintillation occurring in the polar and auroral regions must be taken with caution.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".