High‐Mass Star Formation. III. The Functional Form of the Submillimeter Clump Mass Function
Bibliographic record
Abstract
We investigate the mass function of cold, dusty clumps in 11 low- and high-mass star-forming regions. Using a homogeneous fitting technique, we analyze the shape of each region's clump mass function and examine the commonalities among them. We find that the submillimeter continuum clump mass function in low-mass star-forming regions is typically best fitted by a lognormal distribution, while that in high-mass star-forming regions is better fitted by a double power law. A single power-law clump mass distribution is ruled out in all cases. Fitting all of the regions with a double power law, we find that the mean power-law exponent at the high-mass end of each mass function is α high = -2.4 ± 0.1, consistent with the Salpeter result of α = -2.35. We find no region-to-region trend in α high with the mass scale of the clumps in a given region, as characterized by their median mass. Similarly, nonparametric tests show that the shape of the clump mass function does not change much from region to region, despite the obvious changes in the intrinsic mass scale. This result is consistent with the hypothesis that the clump mass distribution is determined by a highly stochastic process, such as turbulent fragmentation. It may also suggest that the data reduction and analysis techniques strongly affect the shape of the derived mass function.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".