Bibliographic record
Abstract
Abstract Nearby associations of stars which are coeval are important benchmark laboratories because they provide robust measurements of stellar ages. The study of such coeval groups makes it possible to better understand star formation by studying the initial mass function, the binary fraction or the circumstellar disks of stars, to determine how the initially dense populations of young stars gradually disperse to form the field population, and to shed light on how the properties of stars, exoplanets and substellar objects evolve with distinct snapshots along their lifetime. The advent of large-scale missions such as Gaia is reshaping our understanding or stellar kinematics in the Solar neighborhood and beyond, and offers the opportunity to detect a large number of loose, coeval stellar associations for the first time, which evaded prior detection because of their low density or the faintness of their members. In parallel, advances in detection and characterization of exoplanets and substellar objects are starting to unveil the detailed properties of extrasolar atmospheres, as well as population-level distributions in fundamental exoplanet properties such as radii, masses, and orbital parameters. Accurate ages are still sparsely available to interpret the evolution of both exoplanets and substellar objects, and both fields are now ripe for detailed age investigations because we are starting to uncover ever-closer low-density associations that previously escaped detection, as well as exoplanets and ever lower-mass members of more distant open clusters and star-forming regions. In this paper, we review some recent advances in the identification and characterization of nearby associations, the methods by which stellar ages are measured, and some of the direct applications of the study of young associations such as the emergent field of isolated planetary-mass objects.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.174 | 0.162 |
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".