Northern Galactic Molecular Cloud Clumps in Hi-GAL: Clump and Star Formation within Clouds
Bibliographic record
Abstract
Abstract We investigate how the properties of Galactic giant molecular clouds (GMCs) and their denser substructures (clumps) correlate with the local star formation rate (SFR). We trace clouds using the 12CO(3−2) transition, as observed by the CO High Resolution Survey. We identify their constituent clumps using thermal dust emission, as observed by the Herschel infrared GALactic plane survey. We estimate SFRs in these clouds using 70 μm emission. In total, we match 3674 clumps to 473 clouds in position–position–velocity space spanning the Galactic longitude range 10° < ℓ < 56°. We find that more massive clouds produce more clumps and more massive clumps. These clumps have average number densities an order of magnitude greater than their host clouds. We find a mean clump mass fraction of . This mass fraction weakly varies with mass and mass surface density of clouds, and shows no clear dependence on the virial parameter and line width of the clouds. The average clump mass fraction is only weakly dependent upon Galactocentric radius. Although the scatter in our measured properties is significant, the SFR for clouds is independent of clump mass fraction. However, there is a positive correlation between the depletion times for clouds and clump mass fraction. We find a star formation efficiency per freefall time of ϵ ff = 0.15% for GMCs but ϵ ff = 0.37% for clumps.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".