Estimating Geoelectric Fields for Geoelectric Hazard Assessment: An Examination of Data and Models Within Complex Physiographic Zones
Why this work is in the frame
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Bibliographic record
Abstract
During magnetic storms, time-varying geomagnetic fields induce geoelectric fields at the surface that produce geomagnetically induced currents (GICs) within power transmission systems.These GICs can permanently damage these systems, thus motivating research to understand how geoelectric fields behave during storm events.Geomagnetic field data obtained through the INTERMAGNET program are convolved with EarthScope USArray magnetotelluric impedances and impedances collected by Helmholtz Centre for Ocean Research Kiel (GEOMAR), FU Berlin, and GFZ Potsdam to estimate geoelectric variations during a magnetic storm.I consider a magnetic storm ranking G4 occurring between 22 June 2016 to 26 June 2016 recorded at the Brandon, Manitoba (BRD), Fredericksburg, Virginia (FRD), and San Juan (SJG) magnetic observatories.From this, I produce estimated geoelectric fields throughout the duration of a magnetic storm and examine these geoelectric fields across short geographic distances and within the same physiographic zone.This study shows that the geoelectric response of two sites within 200 km of one another can differ by up to two orders of magnitude (4484 mV/km at one site and 41 mV/km at another site 125 km away).I also examine how these geoelectric fields vary across a coastline in order to examine the geomagnetic coast effect's influence on geoelectric hazard assessment.From this, I demonstrate that the application of uniform 1-dimensional conductivity models of the subsurface to wide geographic regions is insufficient to predict the geoelectric hazard at a given site.This necessitates that an evaluation of the 3-dimensional conductivity distribution at a given location is necessary to produce a reliable estimation of how the geoelectric field evolves over the course of a magnetic storm.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 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 it