The science of high-speed imaging of aurora
Bibliographic record
Abstract
Aurora is one of the most distinctive atmospheric phenomena of energy input from the space, and the optical observations of aurora consist of the basis of polar science. The recent advance of image sensors is quite rapid, enabling us to visualize an unprecedentedly fast variation of aurora. The fast variation may be a manifestation of the first-principle physics of aurora generation, which can be applied to broad fields of astrophysics and plasma physics. Pulsations, irregularly switching on and off in the brightness with typical durations of an order of 2 to 20 s, are a fundamental characteristic of post-midnight aurora. Although the pulsating aurora is weak compared with those of quiet arcs or breakups, a cutting-edge sensitive high-speed camera is now capable of detecting the faint aurora with more than several hundred frames per second. It has been found that a fastest-ever-observed fluctuation is superimposed on a pulsating aurora, which is more than an order of magnitude faster than well-known 3 Hz modulation [1]. The generation mechanism of the fast variation remains unknown. In this talk we explain the instrumentation of high-speed imaging and how it sheds light on new science, showing the latest results obtained from the high-speed imaging of aurora at subauroral latitude (AUGO2, Alberta), combined with earlier results obtained at high latitude (PFRR, Alaska) [2, 3]. The cutting-edge ground-based imagers are leading to new findings, which will contribute to make the future Antarctic research project more exciting.
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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 | 0.000 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".