Young, old, massive: Steps to understanding globular cluster formation
Bibliographic record
Abstract
Abstract On observational grounds we now know a huge amount about the characteristics of massive star clusters in galaxies of all types, from the smallest dwarfs to the most massive giants and even into the Intracluster Medium. The old globular clusters (GCs) in particular exhibit a high degree of uniformity across all these environments in their physical properties including scale size, luminosity distribution, metallicity distribution, and age. As survivors of a long period of dynamical evolution, they are “unusual, but not special” among star clusters. The past few years have seen major advances in theoretical modelling that are starting to reveal how these massive star clusters formed in the early stages of galaxy evolution. Several suites of models point to their emergence in GMCs (Giant Molecular Clouds), which provide the turbulent big reservoirs of gas within which star clusters can be built. At cluster masses ∼105M⊙ and above, clusters form hierarchically through a nearly equal combination of direct gas accretion, and mergers with smaller clusters scattered throughout the GMC. GCs and YMCs (young massive clusters) in this high mass range should therefore be composite systems right from birth. To make such high-mass clusters, host GMCs of ∼107M⊙ are needed, and these are most commonly found in galaxies at redshifts z ≳ 2.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".