Probabilistic Forecasting of Ionospheric Scintillation and GNSS Receiver Signal Tracking Performance at High Latitudes
Bibliographic record
Abstract
At high latitudes, phase scintillation occurs predominantly on the day-side in the ionospheric footprint of the magnetospheric cusp, and in the nightside auroral oval. A new technique of probabilistic forecasting of phase scintillation occurrence relative to the arrival time of high-speed solar wind from coronal holes and interplanetary coronal mass ejections has recently been proposed [Prikryl et al. 2012]. Cumulative probability distribution functions for the phase-scintillation occurrence that are ob-tained can be specified for low (below-median) and high (above-median) values of various solar wind plasma parameters. Recent advances in mod-eling of high-speed solar wind and coronal mass ejections, combined with the probabilistic forecasting of scintillation, will lead to improved opera-tional space weather forecasting applications. Scintillation forecasting and mitigation techniques need to be developed to avoid potential costly failures of technology-based Global Navigation Satellite Systems in the near future, in particular during the upcoming solar maximum. The Global Navigation Satellite Systems receiver-tracking performance during severe scintillation conditions can be assessed by the analysis of receiver phase-locked-loop jitter. Tracking jitter maps [Sreeja et al. 2011] offers a potentially useful tool to provide users with expected tracking conditions, if based on scintillation predictions as proposed above. Scintillation in-dices are obtained from L1 GPS data collected with the Canadian High Arctic Ionospheric Network. Combined with high rate amplitude and phase data, they can be used as input to receiver tracking models to de-velop scintillation mitigation techniques.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 teacher head, 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".