A Bayesian chemical evolution model of the DustPedia galaxy M74
Bibliographic record
Abstract
ABSTRACT We introduce a new, multizone chemical evolution model of the DustPedia galaxy M74, calibrated by means of Markov Chain Monte Carlo methods. We take into account the observed stellar and gas density profiles and use Bayesian analysis to constrain two fundamental parameters characterizing the gas accretion and star formation time-scale, i.e. the infall time-scale τ and the SF efficiency ν, respectively, as a function of galactocentric radius R. Our analysis supports an infall time-scale increasing with R and a star formation efficiency decreasing with R, thus supporting an ‘Inside-Out’ formation for M74. For both τ and ν, we find a weaker radial dependence than in the Milky Way. We also investigate the dust content of M74, comparing the observed dust density profile with the results of our chemical evolution models. Various prescriptions have been considered for two key parameters, i.e. the typical dust accretion time-scale τ0 and the mass of gas cleared out of dust by a supernova remnant, Mclear, regulating the dust growth and destruction rate, respectively. Two models with a different current balance between destruction and accretion, i.e. with an equilibrium and a dominion of accretion over destruction, can equally reproduce the observed dust profile of M74. This outlines the degeneracy between these parameters in shaping the interstellar dust content in galaxies. Our methods will be extended to more DustPedia galaxies to shed more light on the relative roles of dust production and destruction.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".