A Ground-Up Data-Driven Approach to Distinguishing Magnetospheric Sources of Geomagnetically Induced Currents > 10 A during the 17 March 2013 Event
Bibliographic record
Abstract
We combine wavelet analysis and data fusion to investigate geomagnetically induced currents (GICs) on the Mäntsälä pipeline and the associated horizontal geomagnetic field, BH, variations during the late main phase of the 17 March 2013 geomagnetic storm. The wavelet analysis decomposes the GIC and BH signals at increasing ‘scales’ to show distinct multi-minute spectral features around the GIC spikes. Four GIC spikes > 10 A occurred while the pipeline was in the dusk sector – the first sine-wave-like spike at ~16 UT was ‘compound.’ It was followed by three ‘self-similar’ spikes two hours later. The contemporaneous multi-resolution observations from ground-(magnetometer, SuperMAG, SuperDARN), and space-based (AMPERE, TWINS) platforms capture multi-scale activity to reveal two magnetospheric modes causing the spikes. The GIC at ~16 UT occurred in two parts with the negative spike associated with a transient sub-auroral eastward electrojet that closed a developing partial ring current (PRC) loop, whereas the positive spike developed with the arrival of the associated mesoscale flow-channel in the auroral zone. The three spikes between 18-19 UT were due to bursty bulk flows (BBFs). We attribute all spikes to flow-channel injections (substorms) of varying scales. We use previously published MHD simulations of the event to substantiate our conclusions, given the dearth of timely in-situ satellite observations. Our results show that multi-scale magnetosphere-ionosphere activity that drives GICs can be understood using multi-resolution analysis. This new framework of combining wavelet analysis with multi-platform observations opens a research avenue for GIC investigations and other space weather impacts.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Open science | 0.001 | 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".