Extracting an ionospheric phase scintillation index based on 1 Hz GNSS observations and its verification in the Arctic region
Why this work is in the frame
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Bibliographic record
Abstract
The ionospheric scintillation, as one of the astronomical disasters occurring frequently in Arctic regions, poses great challenges to GNSS positioning navigation and timing (PNT) services. This calls for an urgent need in studying and effectively monitoring the scintillation to overcome its adverse impact. With the capability of high frequency sampling, ionospheric scintillation monitoring receivers (ISMR) are usually required to monitor the ionospheric scintillation, but the distribution of ISMR restricts the comprehensive monitoring in larger areas (such as the Arctic region). Therefore, based on GNSS observations with 1 Hz sampling, this paper studies the relevant empirical parameters and methods of extracting the ionospheric scintillation signal from the carrier phase observations by using geodetic detrending, precise point positioning and wavelet transform techniques, to construct a new phase scintillation index, which can be used to monitor the ionospheric scintillation. Its effectiveness and accuracy are verified by 188-day observations from 11 stations provided by the Canadian High Arctic Ionospheric Network (CHAIN). The results show that, compared with the commonly used ROTI index, both the scintillation index proposed in this paper and ROTI can effectively detect the occurrence of ionospheric scintillation, but the scintillation index proposed in this paper has a better correlation with the phase scintillation index given by ISMR, especially during periods with strong ionospheric scintillation, indicating that the proposed scintillation index has better ionospheric scintillation monitoring capability.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 it