Energy Flux and Conductance from Meso-Scale Auroral Features Observed by All-Sky-Imagers
Bibliographic record
Abstract
Earth’s Magnetosphere-Ionosphere-Thermosphere system is inseparably coupled, with driving from above and below by various terrestrial and space weather phenomena. Global models have done well at capturing large-scale effects, but currently do not capture the meso-scale (~10s-500 km) phenomena which often are locally more intense. As computing power improves, and modeling meso-scales now becomes possible, it is vital to provide data-informed inputs of the relevant drivers. In this presentation, we focus on the energy flux deposited into the ionosphere from the magnetosphere by precipitating particles that result in the aurora, specifically at meso-scales, and the resulting conductance. Thanks to NASA’s THEMIS mission, an array of all-sky-imagers (ASIs) across Canada monitors the majority of the nightside auroral oval at a 3 second cadence, providing a global view at temporal & spatial resolutions required to study the aurora on meso-scales. Thus, we present 2-D maps over time of the energy flux, energy, and conductance that result from the aurora during solar storms and substorms, including those features at meso-scales. We determine conductance using the ASI-determined eflux and energy as inputs to the Boltzman Three Constituent (B3C) auroral transport code, compare values with Poker Flat ISR observations, and find a good comparison. We find that meso-scale aurora contributes at least 60-70% of the total precipitated energy flux during the first ten minutes of a substorm. Our results can be utilized by the broad community, for example, as inputs to atmospheric models or as the resulting conductance from precipitation inferred by magnetospheric models or satellite observations.
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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) | 0.002 | 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".