Characterization of Transient Geomagnetic Fluctuations and Associated Rapid Ionospheric Currents
Bibliographic record
Abstract
Disturbances in the magnetosphere-ionosphere system cause changes in the geomagnetic field that result in ground induced currents (GICs) that are potentially hazardous to electrical systems on Earth. Harmful GICs are driven by magnetic field fluctuations with timescales generally falling in the range of 1-10 minutes; much less attention has been placed on geomagnetic field fluctuations with short timescales (< 60 seconds) because they cause transient induced currents (TICs) that have not been considered to pose a legitimate threat to electrical systems since they are similar to electrical transients due to lightning. On the contrary, short-timescale magnetic field fluctuations have been found to be capable of coupling directly to power grids and electrical systems, inducing substantial voltages without first flowing in the ground. This ionospheric current coupling poses a potential threat to any of these systems, especially electronic equipment with low operating voltage or that does not have surge protection. Transmitting devices that are at risk by such currents are becoming increasingly more prevalent in society with the growth of the Internet of Things (IoT) network. Our characterization of transient magnetic field perturbations provides detail on short-timescale changes of the magnetosphere-ionosphere coupled system and supports the assessment of possible hazards to technological infrastructure on Earth. This research is enabled by modern magnetometers, both ground- and space-based, with high sampling rate capabilities that allow for the measurement of transient surface magnetic field fluctuations with short-timescales. We present the characteristics of transient magnetic field changes observed by the MACCS array in Arctic Canada by selecting events recorded on the ground and analyzing the behavior of the electromagnetic fluctuations within the ionosphere and magnetosphere during such events.
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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 | 0.000 |
| 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 | 0.000 | 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".