Modeling Geomagnetically Induced Currents Using Geomagnetic Indices and Data
Bibliographic record
Abstract
The possibilities of forecasting geomagnetically induced currents (GIC) in power transmission networks are dependent on the success in modeling these currents. To provide a valuable user-oriented forecast, modeling and proper evaluation of the models using GIC data is important. Many forecasts of geomagnetic storms are presented in terms of geomagnetic indices. Using the GIC data from measuring sites on three power systems in aurora and subauroral regions we estimate the correlation of 3-hourly peak GIC with global geomagnetic indices (3-h ap) and 1 h peak GIC with hourly magnetic range and peak dB/dt values. Geomagnetic 1-min data were used with physics-based and empirical models of the earth and power system response to calculate GIC. These calculated GIC were tested by determining the correlation with measured GIC. Our results show that local geomagnetic indices are better correlated with peak GIC values than are global indices in describing GIC. Correlation coefficients for local (global) indices are 0.9 (0.8) for two subauroral sites and 0.8 (0.7) for an auroral site. Tests of the correlation between 1 min dB/dt or calculated electric field values with measured GIC show a strong directional sensitivity. The direction of peak correlation is different at different sites and is consistent with the direction of power lines. Correlation coefficients for datasets of peak 1-h or 3-h values were higher than for 1-min datasets. This shows that there is a closer relationship between the "envelopes" of geomagnetic disturbances and GIC than between the detailed variations themselves.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".