Characterizing auroral absorption based on geomagnetic hourly range
Bibliographic record
Abstract
Energetic electron precipitation is a source of both ionospheric and geomagnetic disturbances. The resultant increased ionization in the auroral oval leads to the absorption of high frequency radio waves in the auroral zone, or auroral absorption. Auroral absorption is typically characterized by global geomagnetic activity indices, such as the Kp index. In this paper the hourly range of the magnetic field (HR) is examined as an alternative to the 3-hour Kp index for describing the dynamic and localized features of auroral absorption represented by the hourly range of absorption (HRA). Kp, magnetometer, and riometer data were examined for a 3-year period for stations spread across typical auroral latitudes. A general linear relationship was shown to exist between Kp and LOG(HRA) for Kp<4; for Kp≥4 the correlation was weaker. A stronger linear correlation was demonstrated between LOG(HRA) and LOG(HR) for HR >50 nT, characterized by a correlation coefficient of R=0.63. Increased variability in the relationship between HRA and Kp was attributed to the following factors: the variability of the magnetic field within the 3-hour window characterized by the Kp-index which was better represented by a 1-hour HR; the dependence of the Kp index on sub-auroral magnetic data which is not subject to the energetic electron precipitation experienced within the auroral region; and reduced statistics for Kp>6.
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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 | 0.001 | 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) | 0.001 | 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".