Dust evolution with MUPPI in cosmological volumes
Bibliographic record
Abstract
ABSTRACT We study the evolution of dust in a cosmological volume using a hydrodynamical simulation in which the dust production is coupled with the MUPPI (MUlti Phase Particle Integrator) sub-resolution model of star formation and feedback. As for the latter, we keep as reference the model setup calibrated previously to match the general properties of Milky Way-like galaxies in zoom-in simulations. However, we suggest that an increase of the star formation efficiency with the local dust-to-gas ratio would better reproduce the observed evolution of the cosmic star formation density. Moreover, the paucity of quenched galaxies at low redshift demands a stronger role of active galactic nucleus feedback. We tune the parameters ruling direct dust production from evolved stars and accretion in the interstellar medium to get scaling relations involving dust, stellar mass and metallicity in good agreement with observations. In low-mass galaxies, the accretion process is inefficient. As a consequence, they remain poorer in silicate and small grains than higher mass ones. We reproduce reasonably well the few available data on the radial distribution of dust outside the galactic region, supporting the assumption that the dust and gas dynamics are well coupled at galactic scales.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.002 | 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".