Diurnal auroral occurrence statistics obtained via machine vision
Bibliographic record
Abstract
Abstract. Modern ground-based digital auroral All-Sky Imager (ASI) networks capture millions of images annually. Machine vision techniques are widely utilised in the retrieval of images from large data bases. Clearly, they can play an important scientific role in dealing with data from auroral ASI networks, facilitating both efficient searches and statistical studies. Furthermore, the development of automated techniques for identifying specific types of aurora opens up the potential of ASI control software that would change instrument operation in response to evolving geophysical conditions. In this paper, we describe machine vision techniques that we have developed for use on large auroral image data sets. We present the results of application of these techniques to a 350000 image subset of the CANOPUS Gillam ASI in the years 1993–1998. In particular, we obtain occurrence statistics for auroral arcs, patches, and Omega-bands. These results agree with those of previous manual auroral surveys.Key words. Ionosphere (Instruments and techniques) General (new fields)
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".