An Investigation of Optimal Machine Learning Methods for the Prediction of ROTI
Bibliographic record
Abstract
The rate of the total electron content (TEC)change index (ROTI)can be regarded as an effective indicator of the level of ionospheric scintillation, in particular in low and high latitude regions. An accurate prediction of the ROTI is essential to reduce the impact of the ionospheric scintillation on earth observation systems, such as the global navigation satellite systems. However, it is difficult to predict the ROTI with high accuracy because of the complexity of the ionosphere. In this study, advanced machine learning methods have been investigated for ROTI prediction over a station at high-latitude in Canada. These methods are used to predict the ROTI in the next 5 minutes using the data derived from the past 15 minutes at the same location. Experimental results show that the method of the bidirectional gated recurrent unit network (BGRU)outperforms the other six approaches tested in the research. It is also confirmed that the RMSEs of the predicted ROTI using the BGRU method in all four seasons of 2017 are less than 0.05 TECU/min. It is demonstrated that the BGRU method exhibits a high level of robustness in dealing with abrupt solar activities.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".