A Comparative Study of Estimating Auroral Electron Energy from Ground-Based Hyperspectral Imagery and DMSP-SSJ5 Particle Data
Bibliographic record
Abstract
Aurora, the spectacular phenomenon commonly occurring in high latitudes, is caused by the precipitation of energetic particles penetrating the Earth’s atmosphere. Being the result of solar-terrestrial interactions, electron precipitation significantly contributes to auroral production. To evaluate its magnitude, a physical quantity describing the characteristics of precipitating auroral electrons—their characteristic energy—is adopted. In this paper, this quantity is derived from joint data observed by the ground-based auroral spectroscopic imager located in Antarctica Zhongshan Station and the particle detectors “Special Sensor J5 (SSJ5)” on the Defense Meteorological Satellite Program (DMSP) satellites. A postprocessing scheme of ground-based spectral data is proposed to infer the characteristic energy that successively uses classical brute-force, recursive brute-force and self-consistent approximation strategies for step-up speed improvement. Then, the inferred characteristic energies are compared to the average energies calibrated from the relevant electron data detected by SSJ5 to confirm whether this inference is valid. Regarding DMSP F18/SSJ5, these two energy estimations about auroral electrons deviate slightly from each other and show a strong linear relationship. It sheds light on further applications of the valuable aurora spectral data.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| 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".