GPS phase scintillation at high latitudes during the geomagnetic storm of 17–18 March 2015
Bibliographic record
Abstract
Abstract The geomagnetic storm of 17–18 March 2015 was caused by the impacts of a coronal mass ejection and a high‐speed plasma stream from a coronal hole. The high‐latitude ionosphere dynamics is studied using arrays of ground‐based instruments including GPS receivers, HF radars, ionosondes, riometers, and magnetometers. The phase scintillation index is computed for signals sampled at a rate of up to 100 Hz by specialized GPS scintillation receivers supplemented by the phase scintillation proxy index obtained from geodetic‐quality GPS data sampled at 1 Hz. In the context of solar wind coupling to the magnetosphere‐ionosphere system, it is shown that GPS phase scintillation is primarily enhanced in the cusp, the tongue of ionization that is broken into patches drawn into the polar cap from the dayside storm‐enhanced plasma density, and in the auroral oval. In this paper we examine the relation between the scintillation and auroral electrojet currents observed by arrays of ground‐based magnetometers as well as energetic particle precipitation observed by the DMSP satellites. Equivalent ionospheric currents are obtained from ground magnetometer data using the spherical elementary currents systems technique that has been applied over the ground magnetometer networks in North America and North Europe. The GPS phase scintillation is mapped to the poleward side of strong westward electrojet and to the edge of the eastward electrojet region. Also, the scintillation was generally collocated with fluxes of energetic electron precipitation observed by DMSP satellites with the exception of a period of pulsating aurora when only very weak currents were observed.
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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 | 0.000 | 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".