Tracking patchy pulsating aurora through all-sky images
Bibliographic record
Abstract
Abstract. Pulsating aurora is frequently observed in the evening and morning sector auroral oval. While the precipitating electrons span a wide range of energies, there is increasing evidence that the shape of pulsating auroral patches is controlled by structures in near-equatorial cold plasma; these patches appear to move with convection, for example. Given the tremendous and rapidly increasing amount of auroral image data from which the velocity of these patches can be inferred, it is timely to develop and implement techniques for the automatic identification of pulsating auroral patch events in these data and for the automatic determination of the velocity of individual patches from that data. As a first step towards this, we have implemented an automatic technique for determining patch velocities from sequences of images from the Time History of Events and Macroscale Interactions during Substorms (THEMIS) all-sky imager (ASI) and applied it to many pulsating aurora events. Here we demonstrate the use of this technique and present the initial results, including a comparison between ewograms (east–west keograms) and time series of patch position as determined by the algorithm. We discuss the implications of this technique for remote sensing convection in the inner magnetosphere.
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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 | 0.001 |
| 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 | 0.001 |
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".