An Approach to Interpreting Space Weather Natural Indicators to Evaluate the Impact of Space Weather on High-Latitude Power Systems
Bibliographic record
Abstract
Abstract—Dynamic exploration and development of the Arctic zone of the Russian Federation is inextricably linked to the need to minimize risks to the technosphere, associated, among other things, with space weather effects on power equipment systems operated within the coverage of the auroral oval. At the same time, the concomitant monitoring of space weather parameters and variations of the geomagnetic field in the Arctic is carried out only by means of a small group of satellites and several dozens of magnetic stations located mainly in the USA, Canada, northern and central Europe. It is clear that the current situation practically excludes the possibility of operational diagnostics of the level of geomagnetically induced currents (GIC) for the most part of the Arctic zone of the Russian Federation, where, in fact, the only available indicator of space weather conditions are polar auroras. The paper proposes an approach to interpreting aurora appearance to assess space weather impact on high-latitude infrastructure facilities. Based on the case study of the “Vykhodnoy” substation of the “Severnyi Tranzit” (Northern Transit) main electric transmission line it is shown that when the aurora is observed in the north, at the zenith (overhead), and in the south relative to the observation point, the most probable (30-min average) GIC is 0.08, 0.23, and 0.68 A, respectively. At the same time, the probability of half-hourly average GIC exceeding 2 A (with auroras observed in the north, overhead, and in the south relative to the impacted object) is ~6, ~10, and ~15%, respectively. Finally, the ways to improving the proposed technique and the applicability limits of the approach are 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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".