A Bidirectional Long Short-Term Memory-Based Ionospheric foF2 and hmF2 Models for a Single Station in the Low Latitude Region
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Bibliographic record
Abstract
Equatorial electrojet (EEJ) and the subsequent development of equatorial ionization anomaly (EIA) are responsible for the highly complex and nonlinear variability nature of the ionosphere. Prediction of ionospheric parameters like Ionospheric F2 layer Critical frequency (foF2) and peak height (hmF2) feature at low latitude regions is of significant interest in understanding the ionospheric weather effects on communication and navigation systems. The role of artificial intelligence-based machine learning algorithms is successful in the prediction of ionospheric variability. In this letter, a deep learning model based on Bidirectional long short-term memory (Bi-LSTM) technique is implemented for predicting foF2 and hmF2 parameters. The Bi-LSTM method was trained and tested on one-year (2015) ionospheric foF2 and hmF2 data from Canadian Advanced Digital Ionosonde (CADI) located at Hyderabad, India (17.47 °N, 78.57 °E). Bi-LSTM model captures time sequence processing features using past and present foF2 and hmF2 data samples. It is evident from the results that the Bi-LSTM model performs better than long short-term memory (LSTM), neural networks (NNs), and International Reference Ionosphere (IRI) 2016 models in predicting foF2 and hmF2 values. The performance of the Bi-LSTM model tested and found to better predict ionospheric foF2 and hmF2 features for two significant geomagnetic storms occurred in the year 2015 (March and June).
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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 | 0.000 |
| 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 it