Evaluation of the Solar Quiet Reference Field (SQRF) Model for Space Weather Applications in the South America Magnetic Anomaly
Bibliographic record
Abstract
Abstract In the present work, we evaluate the accuracy of the Solar Quiet Reference Field (SQRF) model for forecasting and predicting the geomagnetic solar quiet (Sq) daily field variation in the South America Magnetic Anomaly (SAMA) region. The model simulates the monthly average horizontal field of the geomagnetic quiet (Sq-H) daily variation solving a set of functional fitting equations for the specified geographic coordinates. We carried out two comparisons between the simulated and observational data of the Sq-H field. The first part attempts to evaluate the accuracy for predicting the Sq-H field over Medianeira (25.30°S, 54.11°W, dip angle: -33.45°) by using linear interpolation on the SQRF coefficients. The second part of the analysis attempts to evaluate the accuracy for forecasting the quiet daily field variation over Cachoeira Paulista (22.70°S, 45.01°W, dip angle: -38.48°). The results of the simulation for both locations show that this empirical model presents a good agreement with the Sq-H field obtained from the magnetic field data. The accuracy of the SQRF model (high correlation, r>0.9) provides a high potential for estimating and forecasting geomagnetic quiet daily field variation for space weather applications. Therefore, this model could be useful in the Sq-H field regions near of SAMA.
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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) | 0.000 | 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 | 0.001 | 0.000 |
| 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".