The Early Evolution of Stars and Exoplanet Systems: Exploring and Exploiting Nearby, Young Stars (an Astro2020 Science White Paper)
Bibliographic record
Abstract
Our knowledge of the population of young (age <=750 Myr) stars that lie within ~120 pc of the Sun is rapidly accelerating. The vast majority of these nearby, young stars can be placed in kinematically coherent groups (nearby, young moving groups; NYMGs). NYMGs and their member stars afford unmatched opportunities to explore a wide variety of aspects of the early evolution of stars and exoplanet systems, including stellar initial mass functions and age determination methods; the magnetic activities and high-energy radiation environments of young, late-type stars; the dynamics of young binary and hierarchical multiple systems; the late evolutionary stages of circumstellar disks; and, especially, direct-imaging discovery and characterization of massive young exoplanets. In this Astro2020 Science White Paper, we describe how our understanding of these and many other aspects of the early lives of stars and planetary systems is ripe for progress over the next decade via the identification and study of NYMG members with present and next-generation facilities and instruments.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".