White-light solar corona structure observed by naked eye and processed images
Bibliographic record
Abstract
ABSTRACT Light and dark adaptation and luminance contrast enhancement are well-known characteristics of human vision that allow us to observe a wide range of light intensity not fully captured in standard camera images. The naked-eye observations of total eclipses, some recorded with spectacular detail in artists’ paintings, reveal structure that is consistent with images obtained by telescopes equipped with recording media. The actual shape of the corona during a total eclipse depends not only on the phase of the solar cycle but, as can be simply demonstrated, also on the day-to-day variability and spatial distribution of coronal intensity that is determined by solar surface magnetic fields, including the locations of coronal holes that are the sources of high-speed solar wind causing geomagnetic storms. The latter were very similar for the eclipses in 1932, 1994, and 2017, which is the main reason why the naked-eye observations, as well as the processed images (1994 and 2017), of the white-light corona displayed very similar shapes. White-light corona image processing is a useful technique to enhance the contrast to observe fine-scale structure that is consistent with the physics of the solar atmosphere shaped by the magnetic field drawn out into the interplanetary space by solar wind.
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 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 | 0.000 | 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) | 0.000 | 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".