Investigating energetic electron precipitation through combining ground‐based and balloon observations
Bibliographic record
Abstract
Abstract A detailed comparison is undertaken of the energetic electron spectra and fluxes of two precipitation events that were observed in 18/19 January 2013. A novel but powerful technique of combining simultaneous ground‐based subionospheric radio wave data and riometer absorption measurements with X‐ray fluxes from a Balloon Array for Relativistic Radiation‐belt Electron Losses (BARREL) balloon is used for the first time as an example of the analysis procedure. The two precipitation events are observed by all three instruments, and the relative timing is used to provide information/insight into the spatial extent and evolution of the precipitation regions. The two regions were found to be moving westward with drift periods of 5–11 h and with longitudinal dimensions of ~20° and ~70° (1.5–3.5 h of magnetic local time). The electron precipitation spectra during the events can be best represented by a peaked energy spectrum, with the peak in flux occurring at ~1–1.2 MeV. This suggests that the radiation belt loss mechanism occurring is an energy‐selective process, rather than one that precipitates the ambient trapped population. The motion, size, and energy spectra of the patches are consistent with electromagnetic ion cyclotron‐induced electron precipitation driven by injected 10–100 keV protons. Radio wave modeling calculations applying the balloon‐based fluxes were used for the first time and successfully reproduced the ground‐based subionospheric radio wave and riometer observations, thus finding strong agreement between the observations and the BARREL measurements.
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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) | 0.000 | 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".