Sensitivity of Ground Magnetometer Array Elements for GIC Applications I: Resolving Spatial Scales With the BEAR and CARISMA Arrays
Bibliographic record
Abstract
Abstract Geomagnetically induced currents (GICs) can be driven in terrestrial electrical power grids as a result of the induced electric fields arising from geomagnetic disturbances (GMD) resulting from the dynamics of the coupled magnetosphere‐ionosphere‐ground system. However, a key issue is to assess an optimum spacing for the magnetometer stations in order to provide appropriate monitoring of the GIC‐related GMD. Here we assess the vector correlation lengths of GMD and related amplitude occurrence distribution of the variations of horizontal magnetic field dB H / dt . Specifically, we study the GMD response to two storm‐time substorms using data from two magnetometer arrays, the Baltic Electromagnetic Array Research Project in Scandinavia and the Canadian Array for Realtime Investigations of Magnetic Activity array in North America, so as to determine the appropriate magnetometer spacing in latitude and longitude, for monitoring and assessing GIC risk. We find that although magnetic disturbances are well‐correlated up to distances of several hundred kilometers at mid‐latitudes, the vector correlation length rapidly drops off for station separations of less than 100 km within the auroral oval. In general geomagnetic fluctuations are stronger and more localized in the auroral zone. Since the auroral oval is pushed equatorward during intense magnetic storms, we highlight that networks using a station separation of ∼200 km should provide an excellent basis for monitoring both small and large scale geomagnetic disturbances. A monitoring network with this station spacing is recommended as being appropriate for assessing the role of GMD in driving GICs in the electric 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".