The Varying Mass Distribution of Molecular Clouds Across M83
Bibliographic record
Abstract
The work of Adamo et al. (2015) showed that the mass distributions of young massive stellar clusters were truncated above a maximum-mass scale in the nearby galaxy M83 and that this truncation mass varies with galactocentric radius. Here, we present a cloud-based analysis of ALMA CO($1\to 0$) observations of M83 to search for such a truncation mass in the molecular cloud population. We identify a population of 873 molecular clouds in M83 that is largely similar to those found in the Milky Way and Local Group galaxies, though clouds in the centre of the galaxy show high surface densities and enhanced turbulence, as is common for clouds in high-density nuclear environments. Like the young massive clusters, we find a maximum-mass scale for the molecular clouds that decreases radially in the galaxy. We find the most massive young massive cluster tracks the most massive molecular cloud with the cluster mass being $10^{-2}$ times that of the most massive molecular cloud. Outside the nuclear region of M83 ($R_{g}>0.5$ kpc), there is no evidence for changing internal conditions in the population of molecular clouds, with the average internal pressures, densities, and free-fall times remaining constant for the cloud population over the galaxy. This result is consistent with the bound cluster formation efficiency depending only on the large-scale properties of the ISM, rather than the internal conditions of individual clouds.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".