Estimating Ionospheric Phase Scintillation Indices in the Polar Region from 1 Hz GNSS Observations Using Machine Learning
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Bibliographic record
Abstract
Ionospheric scintillation represents a disturbance phenomenon induced by irregular electron density variations, predominantly occurring in equatorial, auroral, and polar regions, thereby posing significant threats to Global Navigation Satellite Systems (GNSS) performance. Polar regions in particular confront distinctive challenges, including the sparse deployment of dedicated ionospheric scintillation monitoring receiver (ISMR) equipment, the limited availability of strong scintillation samples, severely imbalanced training datasets, and the insufficient sensitivity of conventional Deep Neural Networks (DNNs) to intense scintillation events. To address these challenges, this study proposes a modeling framework that integrates residual neural networks (ResNet) with the Synthetic Minority Over-sampling Technique for Regression with Gaussian Noise (SMOGN). The proposed model incorporates multi-source disturbance features to accurately estimate phase scintillation indices (σφ) in polar regions. The methodology was implemented and validated across multiple polar observation stations in Canada. Shapley Additive Explanations (SHAP) interpretability analysis reveals that the rate of total electron content index (ROTI) features contribute up to 64.09% of the predictive weight. The experimental results demonstrate a substantial performance enhancement compared with conventional DNN models, with root mean square error (RMSE) values ranging from 0.0078 to 0.038 for daytime samples in 2024, and an average coefficient of determination (R2) consistently exceeding 0.89. The coefficient of determination for the Pseudo-Random Noise (PRN) path estimation results can reach 0.91. The model has good estimation results at different latitudes and is able to accurately capture the distribution characteristics of the local strong scintillation structures and their evolution patterns.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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 it