Utilizing optical flow technique to understand plasma convection
Bibliographic record
Abstract
Plasma convection is a fundamental process of mass and energy transport within our solar system. In Earth’s magnetosphere, convection models often underestimate, or even fail to identify the contributions of dynamic ionospheric mesoscale (10s-100s km) structures that are responsible for significant energy transfer within the magnetosphere-ionosphere coupled system. The most used convection model relies on data from radars, which operates on spatial scales of approximately 50 km, with a temporal resolution of 2 minutes. In contrast, modern red-line all-sky cameras have a spatial resolution on the order of 1 km and temporal resolution of 3 s. These cameras respond to low energy precipitating electrons, which makes them sensitive tracers of magnetospheric convection, and sensitive to mesoscale structures that may be missed by radars. In recent years, the deployment of new cameras has expanded the coverage to include most of the auroral oval and polar cap above the North American continent. Despite their potential for monitoring and studying ionospheric convection, currently only rudimentary techniques have been applied to measure the motion of these optical structures. In this work, we show initial results of optical flow calculations to analyze the motion of optical structures observed with the new red-line all-sky cameras. Optical flow calculations represent the apparent motion of objects in consecutive frames. The result of this technique provides two-dimensional flow fields, which has enabled us to enhance our understanding of ionospheric electric fields. Finally, perform a validation analysis by comparing the optical flow calculations and incoherent scatter radar measurements.
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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 | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".