Bibliographic record
Abstract
Introduction A decade has passed since the first discovery of an extrasolar planet by Mayor and Queloz (1995) and its confirmation by Marcy et al. (1997). Since this groundbreaking discovery, about 190 planets have been found around nearby stars, as of May 2006 (Schneider, 2006). Here we shall review the main methods astronomers use to detect extrasolar planets, and the data we can derive from those observations. Aitken (1938) examined the observational problem of detecting extrasolar planets. He showed that their detection, either directly or indirectly, lay beyond the technical horizon of his era. The basic difficulty in directly detecting planets is the brightness ratio between a typical planet, which shines mainly by reflecting the light of its host star, and the star itself. In the case of Jupiter and the Sun, this ratio is 2.5 × 10 –9 . If we keep using Jupiter as a typical example, we expect a planet to orbit at a distance of the order of 5 astronomical units (AU, where 1 AU = 1.5 × 10 11 m = distance from Earth to Sun) from its host star. At a relatively small distance of 5 parsecs from the Solar System, this translates into a mean angular separation of the sources on the sky of 1 arcsecond. Therefore, with present technology, it is extremely demanding to directly image any extrasolar planet inside the overpowering glare of its host star, particularly from the ground, where the Earth's atmosphere seriously affects the observations.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.006 |
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".