To what degree does a high-energy aurora destroy F-region irregularities?
Bibliographic record
Abstract
Using two separate databases of in situ ionospheric observations, we present case studies and perform a statistical investigation of the link between energetic precipitating particles during the polar night and high-latitude F-region steepening density spectra. Our study covers approximately 3 years of data obtained near the peak of the 24th solar cycle from four Defense Meteorological Satellite Program satellites and from the European Space Agency’s Swarm satellites. Focusing on the midnight sector of the auroral oval, we found that there is a near-perfect co-location between high-energy precipitating particles and occurrence of dissipating F-region plasma density spectra. This is because the precipitating energy flux strongly enhances the E-region Pedersen conductivity, allowing fast and efficient dissipation of kilometer-scale F-region irregularities. Spectra that are possibly non-dissipating are in turn co-located with the distribution of soft electron precipitation. Together, dissipating and non-dissipating density spectra constitute two distinct irregularity regimes. Surprisingly, we also found that efficient dissipation notwithstanding, high-energy precipitating particles cause a net increase in the F-region irregularity power, suggesting that growth and dissipation are interlinked and that some of the observed F-region irregularities may conceivably be generated in the E region. This work is expected to be beneficial for the classification of F-region in situ density spectra and suggests that such density spectra can be used to infer the presence of high-energy or low-energy precipitations based on spectral properties.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| 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".