Impact of Geomagnetic Storms on Ionosphere Variability and Precise Point Positioning Application in High Latitudes of the Northern Hemisphere
Bibliographic record
Abstract
Based on the ionospheric scintillation data of the Canadian High Arctic Ionospheric Network (CHAIN), the variation characteristics of ionospheric Total Electric Content (TEC), phase scintillation and Rate of TEC Index (ROTI) were analyzed during the 26 August 2018 geomagnetic storm period. Results show that the TEC anomalies reach 20 TECU on global scale and 6 TECU over Canadian regions, respectively. The occurrence of phase scintillations is about 12.6% during the selected stormy day, which is only around 1% during geomagnetic quiet period. The occurrence of ROTI exhibits high correlation with that of phase scintillations during stormy geomagnetic conditions. The impact of ionospheric scintillations on positioning performance was analyzed by means of GPS Precise Point Positioning (PPP). It is proved that the 3D positioning root mean square errors are within 0.4 m for all test stations during the quiet geomagnetic condition. When it comes to the high geomagnetic condition, the magnitude of positioning errors significantly increase, which reaches 0.9 and 1.7 m in horizontal and vertical directions, respectively.
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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 | 0.000 | 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) | 0.000 | 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".