OBSERVATIONAL EVIDENCE OF DYNAMIC STAR FORMATION RATE IN MILKY WAY GIANT MOLECULAR CLOUDS
Bibliographic record
Abstract
ABSTRACT Star formation on galactic scales is known to be a slow process, but whether it is slow on smaller scales is uncertain. We cross-correlate 5469 giant molecular clouds (GMCs) from a new all-sky catalog with 256 star-forming complexes (SFCs) to build a sample of 191 SFC-GMC complexes—collections of multiple clouds each matched to 191 SFCs. The total mass in stars harbored by these clouds is inferred from WMAP free–free fluxes. We measure the GMC mass, the virial parameter, the star formation efficiency ϵ and the star formation rate per freefall time ϵ ff . Both ϵ and ϵ ff range over 3–4 orders of magnitude. We find that 68.3% of the clouds fall within and about the median. Compared to these observed scatters, a simple model with a time-independent ϵ ff that depends on the host GMC properties predicts . Allowing for a time-variable ϵ ff , we can recover the large dispersion in the rate of star formation. This strongly suggests that star formation in the Milky Way is a dynamic process on GMC scales. We also show that the surface star formation rate profile of the Milky Way correlates well with the molecular gas surface density profile.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".