AurorEye: Production of a Portable, Off-the-Shelf Automated All-Sky Aurora Camera
Bibliographic record
Abstract
Abstract: For decades, the aurora research community has used ground-based all-sky imagers (ASIs) to monitor auroral phenomena and correlate these data with other instrumentation on the ground and in space. These science instruments are generally fixed in location and capability as part of a network of similar instruments. Recently, modern consumer off-the-shelf digital cameras have empowered the aurora citizen science community to capture novel auroral phenomena, leveraging their mobility and local knowledge of weather, auroral behavior, and viewing locations. The AurorEye project aims to put flexible, high-resolution, all-sky timelapse cameras in the hands of aurora chasers, integrating with their mobile photography workflow while maintaining low overhead. Prototyped in 2021, AurorEye units have been deployed in a range of auroral and subauroral regions by citizen scientists. AurorEye units have been tested in various modalities, including at the Poker Flat Research Range to complement the on-site suite of science instruments, and in the field at subauroral latitudes to capture stormtime phenomena. Data have been made publicly available on YouTube with novel visualizations. This presentation will address three points that may inspire other citizen science aurora imaging projects and further collaboration with researchers: (1) Lessons learned and improvements to the hardware, software, and data handling from 3+ years of field testing; (2) Unique methods for visualizing and sharing aurora ASI data; (3) Notable and interesting observations such as a rare “quiet-time” STEVE event in Yellowknife, Canada; and (4) scientific capabilities of AurorEye, including potential citizen science campaigns to coordinate AurorEye observations with satellite passes or notable space weather events. We will also discuss how AurorEye may collaborate with other citizen science projects and invite feedback that enhances the user experience and scientific utility of this ongoing project.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.029 | 0.018 |
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".