On the latitude-dependence of the GPS phase variation index in the polar region
Bibliographic record
Abstract
It has long been established that the presence of irregularities in the ionosphere affects the propagation of radio waves, and in particular radio waves transmitted by GNSS satellites. Ionospheric scintillation is an important physical characteristic of radio wave signals propagating through the ionosphere. It is believed that a reverse backscattering analysis of the scintillating signals measured by GPS receivers on the ground may unveil some knowledge about the ionospheric irregularity structures causing the scattering in the first place and may in turn help understand the physical mechanisms causing the development of these irregularities. The Canadian High Arctic Ionospheric Network (CHAIN) GPS data are used to build probability functions for the phase variation index, which are best fit by the four-parameter Landau distribution. The fits reveal that the distribution scale-parameter value captures the known patterns observed in the phase activity. In particular, the seasonal and the solar cycle dependence are identified. On a yearly basis, the obtained scale parameter is linearly dependent upon the solar radio flux index F10.7. The same parameter increases with the magnetic latitude and reaches a maximum at the cusp region. These results pinpoints the cusp region as a major place where ionospheric structures affecting the radio signal phase are formed before they propagate poleward and equatorward. Furthermore and using particle scattering by a random medium, the distribution scale parameter obtained for σΦmight be considered as the analog of the Total Electron Content. These preliminary results suggest that a detailed analysis of distribution of fluctuations in the radio signal can potentially provide a simple approach to develop scintillation climatological models.
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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 | 0.004 |
| 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 | 0.000 |
| 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".