Assessment of GIC risk due to geomagnetic sudden commencements and identification of the current systems responsible
Bibliographic record
Abstract
Abstract During periods of enhanced geomagnetic activity, geomagnetically induced currents (GIC) flow in power systems potentially causing damage to system components or failure of the system. The largest GIC are produced when there are large rates of change of the geomagnetic field (dB/dt). It is well established that the main phase of a geomagnetic storm, particularly the magnetic substorms occurring during that period, is a cause of large GIC and hence a risk factor for power systems. However, some power system disturbances have been associated with the occurrence of a storm sudden commencement (SSC) prior to the main phase. We investigate the magnetic signature observed on the ground and the associated solar wind and interplanetary magnetic field (IMF) conditions for both SSC and sudden impulse (SI) events, which are grouped together as sudden commencements (SC). SCs are primarily attributed to a sudden enhancement of the magnetopause current. For some events, we show that there is a high‐latitude enhancement (HLE) of the SC amplitude and corresponding dB/dt. The limited spatial extent suggests an ionospheric current source. Examination of the polarity of the change in the X‐component magnetic field shows that the HLE is due to a sudden increase of the ionospheric convection electrojets. The occurrence of the HLE is more prevalent for SSC‐type SCs, SCs caused by coronal mass ejections as opposed to corotating interaction regions, and SCs associated with a large solar wind speed (vsw) prior to the SC or a large Δvsw at the time of the SC.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".