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 estimating and predicting the geomagnetic solar quiet (Sq) daily field variation in the South America Magnetic Anomaly (SAMA) region. This model is based on the data set of fluxgate magnetometers from 12 magnetic stations of the Embrace Magnetometer Network (Embrace MagNet) from 2010 to 2018. The model predicts the monthly average horizontal field of the geomagnetic quiet (Sq-H) daily variation solving a set of equations for the specified geographic coordinates in terms of the solar cycle activity, the day of the year, and the universal time. We carried out two comparisons between the prediction and observational data of the Sq-H field. The first part attempts to evaluate the accuracy for estimating the Sq-H field over Medianeira (MED, 25.30°S, 54.11°W, dip angle: -33.45°) by using linear interpolation on the SQRF coefficients and compared it with the data collected from April to December in 2018. It worth mentioning that none of the datasets collected at MED is part of the dataset used to build the SQRF model, hence the need to do interpolation. The second part of the analysis attempts to evaluate the accuracy for predicting the quiet daily field variation over Cachoeira Paulista (CXP, 22.70°S, 45.01°W, dip angle: -38.48°). The dataset collected at CXP prior to the period analyzed in the present work is part of the dataset used to build the SQRF model. Thus, the accuracy of the prediction is tested using magnetic data outside the time interval considered in the model. The results of the prediction for both locations show that the outputs from this empirical model present 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 predicting geomagnetic quiet daily field variation for space weather applications, improving the scientific insight and capability of space weather prediction centers to predict the variability of the regular solar quiet field variation as reference conditions, which may include areas with no measurements.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".