The JCMT Gould Belt Survey Complete Core Catalogue: core mass function variations between nearby molecular clouds
Bibliographic record
Abstract
ABSTRACT We present a catalogue of dense cores identified in James Clerk Maxwell Telescope (JCMT) Gould Belt Survey SCUBA-2 (Submillimetre Common-User Bolometer Array 2) observations of nearby star-forming clouds. We identified 2257 dense cores using the getsources algorithm, of which 59 per cent are starless, and 41 per cent are potentially protostellar. 71 per cent of the starless cores are prestellar core candidates, suggesting a prestellar core lifetime similar to that of Class 0/I young stellar objects. Higher mass clouds have a higher fraction of prestellar cores compared to protostars, suggesting a longer average prestellar core lifetime. We assessed completeness by inserting critically stable Bonnor–Ebert spheres into a blank SCUBA-2 field: completeness scales as distance squared, with an average mass recovery fraction of $73\pm 6$ per cent for recovered sources. We calculated core masses and radii, and assessed their gravitational stability using the Bonnor–Ebert criterion. Maximum starless core mass scales with cloud complex mass with an index $0.58\pm 0.13$, consistent with the behaviour of maximum stellar masses in embedded clusters. We performed least-squares and Monte Carlo modelling of the core mass functions (CMFs) of our starless and prestellar core samples. The CMFs can be characterized using lognormal distributions: we do not sample the full range of core masses needed to create the stellar initial mass function (IMF). The CMFs of the clouds are not consistent with being drawn from a single underlying distribution. The peak mass of the starless core CMF increases with cloud mass; the prestellar CMF of the more distant clouds has a peak mass $\sim 3\times$ the lognormal peak for the system IMF, implying a $\sim 33$ per cent prestellar core-to-star efficiency.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".