Star Formation Occurs in Dense Gas, but What Does “Dense” Mean?
Bibliographic record
Abstract
Abstract We report results of a project to map HCN and emission toward a sample of molecular clouds in the inner Galaxy, all containing dense clumps that are actively engaged in star formation. We compare these two molecular line tracers with millimeter continuum emission and extinction, as inferred from 13CO, as tracers of dense gas in molecular clouds. The fraction of the line luminosity from each tracer that comes from the dense gas, as measured by mag, varies substantially from cloud to cloud. In all cases, a substantial fraction (in most cases, the majority) of the total luminosity arises in gas below the mag threshold and outside the region of strong millimeter continuum emission. Measurements of toward other galaxies will likely be dominated by such gas at lower surface densities. Substantial, even dominant, contributions to the total line luminosity can arise in gas with densities typical of the cloud as a whole (n ∼ 100 cm−3). Defining the dense clump from the HCN or emission itself, similarly to previous studies, leads to a wide range of clump properties, with some being considerably larger and less dense than in previous studies. HCN and have a similar ability to trace dense gas for the clouds in this sample. For the two clouds with low virial parameters, 13CO is definitely a worse tracer of the dense gas, but for the other four, it is equally good (or bad) at tracing dense gas.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| 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".