Bibliographic record
Abstract
Aims. There exists some consensus that the stellar mass surface density (Σ⋆) and molecular gas mass surface density (Σmol) are the main quantities responsible for locally setting the star formation rate. This regulation is inferred from locally resolved scaling relations between these two quantities and the star formation rate surface density (ΣSFR), which have been extensively studied in a wide variety of works. However, the universality of these relations is debated. Here, we probe the interplay between these three quantities across different galactic environments at a spatial resolution of 150 pc. Methods. We performed a hierarchical Bayesian linear regression to find the best set of parameters C⋆, Cmol, and Cnorm that describe the star-forming plane conformed by Σ⋆, Σmol, and ΣSFR, such that logΣSFR = C⋆logΣ⋆ + CmollogΣmol + Cnorm. We also explored variations in the determined parameters across galactic environments, focusing our analysis on the C⋆ and Cmol slopes. Results. We find signs of variations in the posterior distributions of C⋆ and Cmol across different galactic environments. The dependence of ΣSFR on Σ⋆ spans a wide range of slopes, with negative and positive values, while the dependence of ΣSFR on Σmol is always positive. Bars show the most negative value of C⋆ (−0.41), which is a sign of longer depletion times, while spiral arms show the highest C⋆ among all environments (0.45). Variations in Cmol also exist, although they are more subtle than those found for C⋆. Conclusions. We conclude that systematic variations in the interplay of Σ⋆, Σmol, and ΣSFR across different galactic environments exist at a spatial resolution of 150 pc, and we interpret these variations to be produced by an additional mechanism regulating the formation of stars that is not captured by either Σ⋆ or Σmol. Studying environmental variations in single galaxies, we find that these variations correlate with changes in the star formation efficiency across environments, which could be linked to the dynamical state of the gas that prevents it from collapsing and forming stars, or to changes in the molecular gas fraction.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".