Estimating Precipitating Energy Flux, Average Energy, and Hall Auroral Conductance From THEMIS All-Sky-Imagers With Focus on Mesoscales
Bibliographic record
Abstract
Recent attention has been given to mesoscale phenomena across geospace (∼10 s km to 500 km in the ionosphere or ∼0.5 RE to several RE in the magnetosphere), as their contributions to the system global response are important yet remain uncharacterized mostly due to limitations in data resolution and coverage as well as in computational power. As data and models improve, it becomes increasingly valuable to advance understanding of the role of mesoscale phenomena contributions—specifically, in magnetosphere-ionosphere coupling. This paper describes a new method that utilizes the 2D array of Time History of Events and Macroscale Interactions during Substorms (THEMIS) white-light all-sky-imagers (ASI), in conjunction with meridian scanning photometers, to estimate the auroral scale sizes of intense precipitating energy fluxes and the associated Hall conductances. As an example of the technique, we investigated the role of precipitated energy flux and average energy on mesoscales as contrasted to large-scales for two back-to-back substorms, finding that mesoscale aurora contributes up to ∼80% (∼60%) of the total energy flux immediately after onset during the early expansion phase of the first (second) substorm, and continues to contribute ∼30–55% throughout the remainder of the substorm. The average energy estimated from the ASI mosaic field of view also peaked during the initial expansion phase. Using the measured energy flux and tables produced from the Boltzmann Three Constituent (B3C) auroral transport code (Strickland et al., 1976; 1993), we also estimated the 2D Hall conductance and compared it to Poker Flat Incoherent Scatter Radar conductance values, finding good agreement for both discrete and diffuse aurora.
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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 | 0.000 |
| 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) | 0.000 | 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".