The Influence of the Geoelectric Coast Effect on Geomagnetically Induced Currents
Bibliographic record
Abstract
Geomagnetically induced currents (GICs) in power networks can damage transformers, cause voltage instability and lead to power outages. GICs are driven by an induced voltage in transmission lines due to the induced surface geoelectric field component parallel to the line. It is well-known that an electrically conductive ocean can increase the geoelectric field magnitude on the landward side of the coast. However, limited work has been done to elucidate how the adjacent ocean impacts network GICs. We model GICs using a well-known network model situated adjacent to an ocean. Contrary to the notion that GIC risk is higher in coastal areas, we show that the ocean can cause a decrease in the maximum possible GIC in coastal power networks relative to calculated GICs which exclude coast effects, while increases in GIC due to the ocean can be relatively modest. This is because the geoelectric field only increases in the component perpendicular to the coast but decreases parallel to the coast. Thus, transmission lines parallel to coastlines experience a net decrease in induced voltage along their entire length, while transmission lines perpendicular to coastlines experience an increase in induced voltage that is self-limited by the distance from the coast.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".