Application of <i>F</i><sub>10.7</sub> Index Prediction Model Based on BiLSTM-attention and Chinese Autonomous Dataset
Bibliographic record
Abstract
The F10.7 index is an important indicator of solar activity. Accurate predictions of the F10.7 index can help prevent and mitigate the effects of solar activity on areas such as radio communications, navigation and satellite communications. Based on the properties of the F10.7 radio flux, the prediction model of F10.7 based on BiLSTM-Attention is proposed by incorporating an Attention mechanism on the Bidirectional Long Short-Term Memory Network (BiLSTM). The Mean Absolute Error (MAE) on the Canadian DRAO dataset is 5.38, the Mean Absolute Percentage Error (MAPE) is controlled to within 5% and the correlation coefficient (R) reaches 0.987. It has superior prediction performance compared with other RNN models in both short-term and medium-term prediction. A Conversion Average Calibration (CAC) method is proposed to preprocess the F10.7 data set observed by the Langfang L&S telescope in China. The processed data has high correlation with the DRAO dataset. Based on this dataset the forecasting effectiveness of the RNN series models is compared and analyzed. The experimental results show that both BiLSTM-Attention and BiLSTM models have significant advantages in predicting the F10.7 index and show excellent predictive performance and good stability. The BiLSTM-Attention model has the highest prediction accuracy when forecasting future first-day data, with MAE and MAPE of 11.10 and 8.66, respectively, and the MAPE is always within 15% in the short- and medium-term forecasts. This shows that the proposed model has high generalization ability and can effectively predict the F10.7 data set of DRAO and L&S.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.003 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.006 |
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".