Bayesian ages of local young stellar associations
Bibliographic record
Abstract
Context. Local young stellar associations (LYSAs <50 Myr and <150 pc) are important laboratories to test predictions from star formation theories. Estimating their ages through various dating techniques with minimal biases is thus of paramount importance. Aims. We aim to determine the ages of LYSAs with the expansion rate dating technique. Methods. We estimated the ages of the LYSAs using literature membership lists, publicly available data (astrometry and radial velocities), and a recent open-source Bayesian code that implements the expansion rate method. This code in combination with simple statistical assumptions allowed us to decontaminate, identify possible substructures or populations, and estimate expansion ages. Results. We derive the largest and most methodological homogeneous set of ages of LYSAs. We rediscover three and discover four associations hidden within the literature membership lists of the classical ones. Conclusions. The expansion ages we report here are compatible with literature age estimates. Moreover, our analysis shows that previous age tensions can be explained, in most cases, by the presence of unidentified populations or substructures.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".