Bibliographic record
Abstract
Space weather effects on grounded infrastructure such as high-voltage power networks have been well documented over the past three decades. Current research on Geomagnetically Induced Currents (GIC) seeks to understand both the detailed effects of extreme geomagnetic storms on transformers as well as methods for nowcasting or forecasting the magnitude of such events in real-time, particularly where only sparse measurements may be available. \nWe examine the use of remote observatories (up to 1000 km away) to model the GIC flowing in two hypothetical power grids. The first grid is the benchmark test grid of Horton et al (2012) with 15 ‘transformers’ and the second is a simplified version of the UK power network with around 250 nodes. \nWe place the grids at high geomagnetic latitudes in the auroral to sub-auroral zone around Hudson Bay in Canada and use data from three local magnetic observatories (Baker Lake: BLC; Fort Churchill: FCC and Poste de-la-Baleine: PBQ). We use magnetic data from three large storms of March 1989, June 1991 and October 2003 and a simple land/sea conductance model to calculate the geo-electric field using the thin-sheet modelling method. The GIC flowing with each grid is computed, both separately and jointly, from the magnetic field recorded at the observatories. \nWe find that although the correlation between the GIC flows computed from the different observatories varies with distance to the instrument, the magnitude of the GIC are similar to within around 20%. This suggests that remote observatories can provide useful information for nowcasting GIC flow in a power grid.
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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 | 0.004 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".