PyAuroraX and IDL-AuroraX - data access and analysis support libraries for All-Sky Imager (ASI) data
Bibliographic record
Abstract
AuroraX is working towards being the world's first and foremost data platform for auroral science. The primary objective of AuroraX is to enable mining and exploration of existing and future auroral data, enabling key science and enhancing the benefits of the world's investment in auroral instrumentation. This will be accomplished with the development of key systems/standards for uniform metadata generation and search, image content analysis, interfaces to leading international tools, and a community involvement that includes more than 80% of the world's data providers. AuroraX will significantly lower the barrier of entry to the global network of auroral data, and provide the foundation for efficiency and inter-operability of existing auroral instrument networks and data streams. Use of cutting-edge web frameworks, enhanced metadata, and discovery tools we aim to allow for rapid access and utilization of auroral data by the space physics and citizen science communities. Enabled by key international partnerships and Canada's leading role in auroral instrumentation, AuroraX hopes to fundamentally change the data landscape for auroral science. PyAuroraX and IDL-AuroraX are Python and IDL libraries that provide data access, data analysis support software, and interaction with the AuroraX Search Engine.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.103 | 0.124 |
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".