An Adaptive Forecasting Method for Ionospheric Critical Frequency of <i>F</i>2 Layer
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Bibliographic record
Abstract
Abstract To achieve further improvements in quantitative predictability, a chaos‐based adaptive forecasting method for the critical frequency of the F 2 layer ( f o F 2 ) is proposed for the development of an ionospheric forecasting technique for one hour ahead. This method has three new characteristics. (1) It is based on Volterra filters and it has a simplified structure with easy implementation. (2) Based only on past measured data, it can forecast f o F 2 values without the requirement for past or forecast values of any solar and geomagnetic indices. (3) It can achieve high forecast accuracy with a small training dataset of 27 days (one solar rotation period). Diurnal, seasonal, and annual comparisons of measured and forecasted f o F 2 values are presented to illustrate the applicability and suitability of the proposed method. Statistical results reveal that the f o F 2 values calculated using the proposed model are consistent with the trend of measurements irrespective of whether geomagnetic conditions are quiet or disturbed. The average RMSE and RRMSE values were 0.86 MHz and 17.36%, respectively, when using measured data from periods of past 27 days during 2008–2015. The proposed method has potential to forecast other ionospheric characteristic parameters, and that it could achieve satisfactory regional or global 1‐hr forecasting if combined with a spatial reconstruction technique.
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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 | 0.001 |
| 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