The dust and cold gas content of local star-forming galaxies
Bibliographic record
Abstract
ABSTRACT We use dust masses (Mdust) derived from far-infrared data and molecular gas masses (Mmol) based on CO luminosity to calibrate proxies based on a combination of the galaxy Balmer decrement, disc inclination, and gas metallicity. We use such proxies to estimate Mdust and Mmol in the local SDSS sample of star-forming galaxies (SFGs). We study the distribution of Mdust and Mmol along and across the main sequence (MS) of SFGs. We find that Mdust and Mmol increase rapidly along the MS with increasing stellar mass (M*), and more marginally across the MS with increasing SFR (or distance from the relation). The dependence on M* is sub-linear for both Mdust and Mmol. Thus, the fraction of dust (fdust) and molecular gas mass (fmol) decreases monotonically towards large M*. The star formation efficiency (SFE, inverse of the molecular gas depletion time) depends strongly on the distance from the MS and it is constant along the MS. As nearly all galaxies in the sample are central galaxies, we estimate the dependence of fdust and fgas on the host halo mass and find a tight anticorrelation. As the region where the MS is bending is numerically dominated by massive haloes, we conclude that the bending of the MS is due to a lower availability of molecular gas mass in massive haloes rather than a lower efficiency in forming stars.
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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 | 0.001 |
| 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 | 0.000 |
| 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".