Proxy Index Derived From All Sky Imagers for Space Weather Impact on GPS
Bibliographic record
Abstract
Abstract Global Positioning System (GPS) signals passing through the auroral ionosphere, which exhibits multiscreen electron density structuring, maybe scintillated causing observation and possibly positioning errors. It is advantageous to determine the magnitude of GPS signal scintillation associated with a given level of auroral brightness observed around the signal's ionospheric pierce point (IPP). Such information would enable the exploitation of auroral image observations in space weather monitoring and help in assessment of impact on infrastructure/services reliant on GNSS. Studies have observed a general positive correlation between auroral brightness and GPS phase scintillation but not a definite one‐to‐one relationship. In this study a correlation coefficient of 0.38 is observed between the phase scintillation and the level of auroral arc brightness around the GPS signal's raypath for a data set of 292 events in the Canadian sector. Alternatively, a new pseudo‐scintillation index, Rate of change of Brightness index, is introduced in this study which is derived from the changing auroral brightness around the satellite's IPP. This Rate of change of Brightness index is highly positively correlated (correlation coefficient: 0.75) with GPS phase scintillation. Spatial and spectral relationships between auroral brightness around the satellite's IPP and phase scintillation were also analyzed. It is observed that probability of GPS signals experiencing phase scintillation is high when the auroral brightness around the satellite's IPP has dominant fluctuations in the frequency band ~0.06 to 0.16 Hz. These results indicate that GPS signal scintillation is related to the dynamics of the brightness around the satellite's IPP as the satellite signal propagates through the aurora.
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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 | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".