Bibliographic record
Abstract
In section 2.19, we outlined what was presently known about star formation, and the early evolution of stars. There are several types of variability which are specifically or predominantly found among young stars. Most or all of these would be found in a given star, such as the sun, at various times in its pre-main-sequence (PMS) lifetime. We must always remember that the universe we see is a snapshot of millions of stars, seen at various random stages of their evolution. The PMS stage lasts only a few million years, so stars spend only a small fraction of their lifetime in it. But every star passes through this stage once. PMS variables are often called nebular variables , because, being young, they are usually found in or near the nebulae from which stars are born. Or they may be called Orion population because the Orion region is a nearby, active site of star formation. One of the challenges in studying these stars is the fact that they are usually found within clouds of gas and dust, which will obscure or hide them at visible wavelengths. Radio observations have therefore been useful, and new sub-millimeter and mid-IR facilities such as the Atacama Large Millimeter Array and the James Webb Space Telescope , respectively, will be ideally suited for studying star formation. A PMS star could also be an eclipsing or rotating variable, if it had a close companion, or a spotted surface. It could even be a pulsating variable, if it was located in an instability strip. We shall concentrate here on types of objects, and of variability, which are unique to PMS stars.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.013 |
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".