It's Not Easy Being Green: Quantitative Modeling of STEVE's Picket Fence Emissions Driven by Local Parallel Electric Fields
Bibliographic record
Abstract
The vibrant green streaks of the 'picket fence' typically appear below a STEVE arc in the subauroral sky, at lower latitudes than the auroral oval. Recent studies suggest that, despite its aurora-like appearance, the picket fence may not be driven by magnetospheric particle precipitation but instead by local electric fields parallel to Earth's magnetic field. In this study, we investigate this hypothesis by quantitatively comparing observed picket fence optical spectra with emissions generated in a kinetic model driven by parallel electric fields in a realistic neutral atmosphere. We find that sufficiently large parallel electric fields can reproduce the observed ratio of N2 first positive to oxygen green line emissions, without producing N2+ first negative emissions. We find that, at a typical picket fence altitude of 110 km, parallel electric fields between 40 and 70 Td (~80 to 150 mV/m at 110 km) result in calculated spectral features consistent with observed ones, providing a benchmark for future observational and modeling studies. Additionally, we review studies which have identified similar features to the picket fence in the aurora, suggesting that a similar mechanism may be at work there. Since visible and ultraviolet auroral emissions are increasingly used to infer magnetospheric activity, it is important to better understand and quantify potential sources of emission beyond particle precipitation.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".