POWER AND PIPELINES (GROUND SYSTEMS)
Bibliographic record
Abstract
Geomagnetically induced currents (GIC) in technological sys-tems, such as electric power transmission systems, oil and gas pipelines, telecommunication cables and railway equipment, are a manifestation of space weather at the earth's surface. In power systems, GIC cause saturation of transformers, which may lead to problems in the operation of the system, and even to a collapse of the whole system and to permanent damages of transformers. The best-known GIC effect occurred in March 1989 when the Québec province in Canada suffered from an electric black-out for about nine hours. The corrosion rate may be increased in pipelines when GIC flows from the pipe into the soil, and the associated voltages can disturb pipeline sur-veys and the cathodic protection. This paper summarizes GIC effects on power systems and pipelines. GIC research associ-ated with the Finnish high-voltage power system is discussed. GIC measurement data on a natural gas pipeline and Sweden is presented. The electric field observed at the earth’s surface during a geomagnetic disturbance is the key quantity for the calculation of GIC magnitudes. It depends on currents in the ionosphere and on currents flowing within the earth. The theo-retical modelling of the electric field is discussed in this paper. Particular attention is paid to the complex image method, which permits accurate and fast computations of the electric field and which is thus suitable for time-critical applications like GIC forecasting. 1.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.043 | 0.010 |
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".