Temporal and Spatial Distribution of Phase Scintillation and GNSS Positioning Errors in Northern Canada During the 2017 September Geomagnetic Storms
Bibliographic record
Abstract
High latitude phase scintillation is strongly related to geomagnetic storms. It can significantly decrease the Global Navigation Satellite System (GNSS) performance by increasing positioning errors. Based on the scintillation data recorded by 5 ionospheric scintillation monitoring receiver (ISMR) stations, this study investigates the temporal and spatial distributions of phase scintillation occurrence during the 2017 September Geomagnetic storms, which is the severest storm in solar cycle 24. The positioning errors at these ISMR stations are estimated using precise point positioning (PPP) techniques. It is found that both the phase scintillation levels and positioning errors present strong temporal and spatial dependence. The positioning errors can increase to a maximum value of 2.54 m in the up direction. Additionally, the phase scintillation occurrence and the positioning errors are analyzed against the geomagnetic field activities, which are measured by another 5 magnetometer stations nearby the ISMR stations. Correlation between the Geometric Dilution of Precision (GDOP) normalized 3D positioning errors and the geomagnetic field horizontal component is investigated. Results show that large positioning errors tend to relate to strong geomagnetic field activities. This study is beneficial for better understanding the high latitude phase scintillation effects on GNSS positioning. It also helps to develop forecasting models to predict positioning errors during geomagnetic storms.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".