Forecasting of Ionospheric Total Electron Content Data using Autoregressive Distributed Lag Model for Mid-Latitude Region During Solar Minimum and Maximum
Bibliographic record
Abstract
In this study, the relationship between the Total Electron Content (TEC) data and the geomagnetic indices (Dst, AE and Kp) and the solar index F10.7 obtained along with the time of the day from the Space Physics Data Facility, NASA/Goddard Space Flight Center for Baie-Comeau (Baie) station (49.22°N, 6S.15°W) and Schefferville (Sch2) station (54.S3°N, 66.S3°W) Canada, located in the mid-latitude region was investigated for the Solar Minimum (2008) and Maximum (2014) years for prediction of the TEC. The objectives of this study are: i) to investigate the long-term and short-term dependence of TEC on the selected geomagnetic indices and F10.7 index, using an Autoregressive Distributed Lag (ARDL) model, ii) to test the stability of the ARDL model using various diagnostic tests such as the Jarque-Bera (JB) normality test, the serial correlation Lagrange multiplier (LM) test, and Breusch-Pagan-Godfrey heteroskedasticity test and iii) to forecast the TEC data using the ARDL model for the solar minimum and maximum years. The results indicate statistically that during the solar minimum year, the TEC depends only on the time of the day and is independent of the solar and geomagnetic parameters whereas for the solar maximum year it depends on the solar index F10.7 and time of the day. The proposed models passed the various residual diagnostic tests and stability tests. The RMSE for the predicted data was 0.79 for Baie and 0.93 for Sch2 stations for the solar minimum year and 2.00 for Baie and 1.96 for Sch2 for the solar maximum year.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".