Identifying Ionospheric Small‐Scale Currents: A Spatial Correlation Study Using Closely‐Spaced Pairs of Ground Magnetometers
Bibliographic record
Abstract
Abstract The occurrence of small‐scale and intense ionospheric currents that can contribute to geomagnetically induced currents have recently been discovered. A difficulty in their characterization is that their signatures are often only observed at single widely spaced (typically 200–400 km) ground geomagnetic stations. These small‐scale structures motivate the examination of the maximum station separation required to fully characterize these small‐scale signatures. We analyze distributions of correlation coefficients between closely spaced mid‐latitude and auroral zone ground magnetometer stations spanning day to month long intervals to assess the separation distance at which geomagnetic signatures appear in only one station. Distributions were analyzed using periods that included low and high geomagnetic activity. We used data from pairs of magnetometer stations across North America within 200 km of each other, all of which were separated primarily latitudinally. Results show that while measurements remain largely similar up to separations of 200 km, large and frequent differences appear starting at around 130 km separation. Larger differences and lower correlations are observed during high geomagnetic activity, while low geomagnetic activity leads to frequent high correlation even past 200 km separation. Small but identifiable differences can appear in magnetometer data from stations as close as 35 km during high geomagnetic activity. We recommend future magnetometer array deployment in the auroral and sub‐auroral zone to have separations of 100–150 km. This enables the monitoring of large scale effects of geomagnetic storms, better temporal and spatial resolution of substorms, and observations of small‐scale current signatures.
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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 | 0.001 | 0.002 |
| 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) | 0.000 | 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".