Validation of the space weather modeling framework using ground‐based magnetometers
Bibliographic record
Abstract
Geomagnetically induced currents (GICs) can disrupt power grid operations, causing significant interference for many people; therefore, predictions of ground‐based magnetic perturbations and their time derivatives are quite important. This study quantifies how well the University of Michigan's Global MHD code predicts approximately 150 ground‐based magnetometer traces for a number of storm‐time intervals. It is found that in order to accurately represent the magnetic perturbation, Biot‐Savart integrals over the entire hemisphere are needed, especially when calculating the vertical component. The 4 May 1998 storm is examined in detail. The code compares well with some stations, quantified by a normalized root mean squared error and cross correlation, while missing even the general trends for other stations. When multiple magnetometer station perturbations are averaged together, the model does an adequate job in the north and vertical components but reverses the trends in the eastward component. The code does significantly better when comparing an AL‐like index but does not have as strong a variation as the actual data. Comparison of dB/dt in a wide window rather than simultaneously shows better model performance in capturing events but worse in yielding false alarms. It is further found that the MHD code models the magnetic perturbations better in the summer hemisphere than in the winter hemisphere.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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".