Bibliographic record
Abstract
Abstract Understanding the geomagnetic hazard to power systems requires the ability to model the geomagnetically induced currents (GIC) produced in a power network. This paper presents the developments in GIC modeling starting with an examination of fundamental questions about where the driving force for GIC is located. Then we outline the two main network modeling approaches that are mathematically equivalent and show an example for a simple circuit. Accurate modeling of the GIC produced during real space weather events requires including the appropriate system characteristics, magnetic source fields, and Earth conductivity structure. It is shown how multiple voltage levels can be included in GIC modeling and how the network configuration affects the GIC values. Magnetic source fields can be included by using “plane wave” or line current models or by using geomagnetic observatory data with an appropriate interpolation scheme. Earth conductivity structure can be represented by 1‐D, 2‐D, or 3‐D models that are used to calculate the transfer function between electric and magnetic fields at the Earth's surface. For 2‐D and 3‐D structures this will involve a tensor impedance function and electric fields that are not necessarily orthogonal to the magnetic field variations. It is now technically possible to include all these features in the modeling of GIC, and various software implementations are being developed to make these features more accessible for use in risk studies.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".