Real-Time Ionosphere Prediction Based on IGS Rapid Products Using Long Short-Term Memory Deep Learning
Why this work is in the frame
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Bibliographic record
Abstract
<h3>Abstract</h3> High-precision ionospheric corrections are essential for precise positioning using low-cost single-frequency GNSS receivers. Although Real-Time Global Ionosphere Maps (RT-GIMs) are available from the International GNSS Service (IGS), their ionospheric predictions continue to rely on networks of globally-distributed GNSS stations and real-time data links. In this paper, we develop a regional real-time ionospheric prediction model based on a long short-term memory (LSTM) deep learning method. Because the GIMs from the IGS are used as prediction bases, the requirement for real-time GNSS data-links is eliminated. A comparison of the ionospheric predictions generated over 24 hours by the proposed method and the IGS GIM revealed a prediction accuracy root mean square error of 0.8 TECU. These results suggest that the proposed model may be suitable for use in real-time applications.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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