Dependencies of GPS Scintillation Indices on the Ionospheric Plasma Drift and Rate of Change of TEC Around the Dawn Sector of the Polar Ionosphere
Bibliographic record
Abstract
Abstract The dependencies of global positioning system (GPS) scintillation indices on ionospheric plasma flow and the rate of change of total electron content (TEC) around the dawn sector for the first time of the polar ionosphere are investigated. The phase scintillation index (σφ) derived from GPS measurements of the Canadian High Arctic Ionospheric Network (CHAIN) shows linear dependencies on both the plasma drift speed measured by the SuperDARN radar and on the rate of change of TEC estimated from the GPS receivers of CHAIN. However, the amplitude scintillation index (S4) does not show any dependence on the plasma flow or the rate of change of TEC. These results further support Wang et al. (2018), https://doi.org/10.1002/2017JA024805 at the noon sector. The dependence of the phase scintillation index on the plasma flow further evidences that the standard phase scintillation index is dominated by refractive variations due to the use of a fixed cut‐off frequency of 0.1 Hz while detrending the phase observable. The dependence of the phase scintillation index on the rate of change of TEC consolidates the dominance of refractive variations inside.
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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 | 0.001 |
| 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".