Extraction of spatially confined small-scale waves from high-resolution all-sky airglow images based on machine learning
Bibliographic record
Abstract
Abstract. Since June 2019, a scanning airglow camera is operated operationally every night at DLR Oberpfaffenhofen (48.09° N, 11.28° E), Germany. It provides nearly all-sky images (diameter 500 km) of the OH* airglow layer (height ca. 85–87 km) with an average spatial resolution of ca. 150 m and a temporal resolution of ca. 2 min. We analyse about three years (941 nights between October 2020 and September 2023) of OH* airglow all-sky images for spatially confined wave structures with horizontal wavelengths of ca. 20 km and less. Such structures are often referred to as ripples and are considered to be instability structures. However, Li et al. (2017) showed that they could also be secondary waves. While ripples move with the background wind, secondary waves do not. To identify small-scale and spatially confined structures, we adapt and train YOLOv7 (You Only Look Once, version 7), a machine learning approach, to determine their position and extent on the sky as well as their horizontal wavelength. Those wavelengths are compared to two-dimensional FFT (Fast Fourier Transform) results. We analyse the seasonal variations in the propagation direction and horizontal wavelengths of these structures and deduce that instability signatures are observed especially in summer. Finally, we introduce a concept for “operating-on-demand” in order to derive energy dissipation rates from our measurements.
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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 | 0.001 | 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 | 0.000 |
| 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 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".