STAR FORMATION EFFICIENCIES AND LIFETIMES OF GIANT MOLECULAR CLOUDS IN THE MILKY WAY
Bibliographic record
Abstract
We use a sample of the 13 most luminous WMAP Galactic free–free sources, responsible for 33% of the free–free emission of the Milky Way, to investigate star formation. The sample contains 40 star-forming complexes; we combine this sample with giant molecular cloud (GMC) catalogs in the literature to identify the host GMCs of 32 of the complexes. We estimate the star formation efficiency GMC and star formation rate per free-fall time ff . We find that GMC ranges from 0.002 to 0.2, with an ionizing luminosity-weighted average 〈 GMC 〉 Q = 0.08, compared to the Galactic average ≈0.005. Turning to the star formation rate per free-fall time, we find values that range up to . Weighting by ionizing luminosity, we find an average of 〈 ff 〉 Q = 0.14–0.24 depending on the estimate of the age of the system. Once again, this is much larger than the Galaxy-wide average value ff = 0.006. We show that the lifetimes of GMCs at the mean mass found in our sample is 27 ± 12 Myr, a bit less than three free-fall times. The GMCs hosting the most luminous clusters are being disrupted by those clusters. Accordingly, we interpret the range in ff as the result of a time-variable star formation rate; the rate of star formation increases with the age of the host molecular cloud, until the stars disrupt the cloud. These results are inconsistent with the notion that the star formation rate in Milky Way GMCs is determined by the properties of supersonic turbulence.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".