Auroral Energy Deposition and Conductance During the 2013 St. Patrick's Day Storm: Meso‐Scale Contributions
Bibliographic record
Abstract
Abstract Geomagnetic storms transfer massive amounts of energy from the sun to geospace. Some of that energy is dissipated in the ionosphere as energetic particles precipitate and transfer their energy to the atmosphere, creating the aurora. We used the Time History of Events and Macroscale Interactions during Substorms (THEMIS) mosaic of all‐sky‐imagers across Canada and Alaska to measure the amount of energy flux deposited into the ionosphere via auroral precipitation during the 2013 March 17 storm. We determined the time‐dependent percent of the total energy flux that is contributed by meso‐scale (<500 km wide) auroral features, discovering they contribute up to 80% during the sudden storm commencement (SSC) and >∼40% throughout the main phase, indicating meso‐scale dynamics are important aspects of a geomagnetic storm. We found that average conductance was higher north of 65° until SYM‐H reached −40 nT. We also found that the median conductance was higher in the post‐midnight sector during the SSC, though localized conductance peaks (sometimes >75 mho) were much higher in the pre‐midnight sector throughout. We related the post‐midnight/pre‐dawn conductance to other recent findings regarding meso‐scale dynamics in the literature. We found sharp conductance peaks and gradients in both time and space related to meso‐scale aurora. Data processing included a new moonlight removal algorithm and cross‐instrument calibration with a meridian scanning photometer and a standard photometer. We compared ASI results to Poker Flat Incoherent Scatter Radar (PFISR) observations, finding energy flux, mean energy, and Hall conductance were highly correlated, moderately correlated, and highly correlated, respectively.
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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) | 0.001 | 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".