Bibliographic record
Abstract
Honorable mention in the 2023 IAU OAE Astrophotography Contest, category Still images with smartphones-mobile devices: Northern Lights - Teepees, by Oanh Vuong. Taken with a smartphone at Cassidy Point, Yellowknife, Canada, on 24 March 2023, this stunning display captured the ethereal Northern Lights painting the night sky. The biting cold of -20°F (-29°C) set the stage for the vibrant hues of the Aurora Borealis, a celestial ballet created by collisions between charged solar particles and the Earth's atmosphere. The Earth’s magnetic field directs the charged particles towards the polar regions, where they interact with the various atoms and molecules in the atmosphere. This natural phenomenon transforms the sky into a canvas of radiant greens, pinks, and purples, casting a mesmerising glow above. The different colours of an aurora are determined by the gases in Earth’s atmosphere, the altitude where the aurora occurs, the density of the atmosphere, and the energy of the charged particles. In general, green is attributed to oxygen molecules, red is associated with high-altitude oxygen molecules, purple and blue are associated with hydrogen and helium, and pink aurorae are typically associated with nitrogen. Against this cosmic backdrop, the teepees of Aurora Village below provide a tranquil contrast to the celestial spectacle unfolding overhead. Preserving the pristine darkness of this location ensures the continued splendour of such awe-inspiring natural light shows. Credit: Oanh Vuong/IAU OAE (CC BY 4.0)
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.702 | 0.447 |
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".