SGCast: A New Forecasting Framework for Multilocation Geomagnetic Data With Missing Traces Based on Matrix Factorization
Bibliographic record
Abstract
Geomagnetic data forecasting plays a critical role for natural disaster institutions to respond promptly or make decisions for the magnetic storm warning, earthquake early warning, and so on. However, the forecasting accuracy is not always reliable due to the spatial correlations among different sites and the temporal correlations from each site measurement update. To address the correlation problems, a novel sparse geomagnetic data forecasting (SGCast) matrix framework is proposed in this study. To be specific, a coupled matrix factorization is proposed to model the sparse multilocation geomagnetic data and spatial correlation, which is demonstrated with an example from seven Chinese cities. After the factorization, two subspaces, location subspace, and temporal subspace are derived. Finally, the future geomagnetic signals are reconstructed based on forecasting temporal subspace and matrix reconstruction. The experimental results from the extensive comparison studies demonstrate the superiority of the proposed SGCast approach compared to the state-of-the-art approaches, with an approximate improvement of the forecasting accuracy as 10%~15%.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".