Characterization of the energy‐dependent response of riometer absorption
Bibliographic record
Abstract
Abstract Ground‐based riometers provide an inexpensive means to continuously remote sense the precipitation of electrons in the dynamic auroral region of Earth's ionosphere. The energy‐dependent relationship between riometer absorption and precipitating electrons is thus of great importance for understanding the loss of electrons from the Earth's magnetosphere. In this study, statistical and event‐based analyses are applied to determine the energy of electrons to which riometers chiefly respond. Time‐lagged correlation analysis of trapped to precipitating fluxes shows that daily averaged absorption best correlates with ∼60 keV trapped electron flux at zero‐time lag, although large variability is observed across different phases of the solar cycle. High‐time resolution statistical cross‐correlation analysis between signatures observed by riometer stations, and assuming electron motion due to gradient and curvature drift, results in inferred energies of 10–100 keV, with a clear maximum in occurrence for 40–60 keV electrons. One event is considered in detail utilizing riometer absorption signatures obtained from several stations. The mean inferred energies for the initial rise time and peak of the absorption after correction for electric field effects were ∼70 keV and ∼60 keV, respectively. The analyses presented provide a means to characterize the energy of electrons to which riometers are responding in both a statistical sense and during the evolution of individual events.
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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 | 0.000 |
| 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".