Bibliographic record
Abstract
Abstract Geomagnetically induced currents (GIC) are a phenomenon well known for its negative effects on the operations of power systems. To efficiently mitigate them requires different types of power system modeling, from GIC to alternating current harmonic generation, to three‐dimensional finite element models of transformers. GIC are initiated by variations of the geomagnetic field in the presence of the conductive Earth, that is, the geophysical variables characterized by continuous frequency spectra, making GIC also exhibit continuous spectra. In order to adequately estimate their variations and peak values for mitigation purposes, an analysis is required of how sampling rate and spectral frequency content impact the measured characteristics of GIC and harmonics. The study is based on the geomagnetic measurements and the power network data (i.e., GIC and harmonics) with high sampling rates recorded during two geomagnetic storms, March 31, 2001 and July 26–27, 2004. Availability of data covering both the source and the result of geomagnetic storm impacts on power grid allows (a) analysis of the influence of spectral content on adequate representation of both geomagnetic and geoelectric variations during the intervals with significant increases in GIC and harmonics and (b) identifying the sampling rate sufficient to usefully represent the network response presented as GIC and harmonics variations. In summary, the adequate sampling rate is suggested and the deficiencies associated with undersampling of the geoelectric and GIC variations are identified and discussed.
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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 | 0.013 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".