Toward the Reconstruction of Substorm‐Related Dynamical Pattern of the Radiowave Auroral Absorption
Bibliographic record
Abstract
Abstract In addition to existing empirical models describing the average distributions of energetic electron precipitation into the auroral ionosphere at different activity levels, we develop and test a semiempirical approach to construct dynamical models describing the recurrent features of spatiotemporal development of auroral absorption in the ionosphere during individual substorms. Its key ingredients are (a) usage of linear prediction filter technique to extract from riometer data the response function to the injection of unit magnitude and (b) characterization of injection parameters by midlatitude magnetic variations caused by the substorm current wedge. Using global riometer network we test the method performance for stations in the middle of auroral zone (at corrected geomagnetic latitudes of 65–67°) where generally the absorption amplitude is largest. In this paper we use the midlatitude positive bay index, recently developed by X. Chu and R. McPherron, to drive the model. We evaluate the model performance, discuss the dynamical properties of energetic electron precipitation as revealed by the linear prediction filter response function analyses, and finally, we discuss possible future improvements of this method intended for both science and applications.
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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 | 0.000 | 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".