All-Sky Imager and Geosynchronous Spacecraft Analysis of Nighttime Magnetic Perturbation Events Observed in Arctic Canada
Bibliographic record
Abstract
Magnetic perturbation events (MPEs) can potentially produce geomagnetically-induced currents within power grids and rail lines. Predicting when and where these events will occur is not yet possible. It is currently unclear what range of auroral and magnetospheric features are correlated with MPEs. Identifying these features will make predicting them easier. In this study we examine the all sky images and magnetically conjugate geosynchronous GOES 13 particle data during 121 MPEs identified in the Kuujjuarapik, Canada magnetometer by Engebretson et al. (2021; 2022). The all sky image data shows a wide range of auroral features associated with MPEs including: auroral streamers, westward traveling surges, pseudo breakups, poleward boundary intensifications, auroral omegas, waves, and vortices. Twenty-Seven of 42 MPEs with all sky images including streamers, auroral omegas, and westward traveling surges are closely associated with high speed earthward flows (HSEFs) in the magnetotail. Similarly, in the GOES 13 magnetic field and particle data we find a range of features including magnetic field dipolarizations, dispersionless and dispersed particle injections, flux increases, flux decreases, flux dropouts, and no changes in the particle flux levels. Forty-five of 121 MPEs with geosynchronous electron data (57 of 121 MPEs with ion data) have particle injections, which are a good proxy for HSEFs. These auroral and spacecraft results suggest HSEFs in the magnetotail are a common magnetospheric source of MPEs. While some of these features are associated with HSEFs, our results indicate that there is a wide range of auroral and magnetospheric features, making predicting MPEs difficult.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".