Study of Substorm‐Related Auroral Absorption: Latitudinal Width and Factors Affecting the Peak Intensity of Energetic Electron Precipitation
Bibliographic record
Abstract
Abstract Previously, suggesting a scenario of “injected and drifting electron cloud” to describe the enhancements and precipitations of energetic electrons during substorms we used the linear prediction filter (LPF) method to build the empirical dynamical model of auroral absorption in the middle of auroral zone, driven by the midlatitude positive bay (MPB) index time series. In this paper, to understand better the relationship between magnetic dipolarization, injection, and energetic electron precipitation, we quantitatively explore correlations between dipolarization proxies (MPB and SML indices, substorm current wedge (SCW) intensity, location, and size) and precipitation‐related auroral absorption in the morning maximum region for 148 isolated substorms. We confirm good correlation of precipitation peak values with dipolarization proxies and show that the absorption amplitude is most strongly controlled by total SCW current and its azimuthal size, and is also influenced by solar wind‐dependent background energetic electron flux in the conjugate plasma sheet. This result confirms the adequacy of our starting assumption that there is a close relationship between the intensities and size of substorm dipolarizations and energetic particle injections. To extend the latitudinal coverage, we computed the LPF response functions for individual riometers of NORSTAR array. Finding similar shapes and amplitudes of their response at latitudes 63°–69° in the regions where maximal precipitation is statistically observed, we suggest, test, and approve that the auroral absorption in the belt 63°–69° may be predicted using a set of LPFs determined empirically in the center of auroral zone for various magnetic local times.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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 teacher head, 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".