METAL DIFFUSION IN SMOOTHED PARTICLE HYDRODYNAMICS SIMULATIONS OF DWARF GALAXIES
Bibliographic record
Abstract
ABSTRACT We perform a series of smoothed particle hydrodynamics simulations of isolated dwarf galaxies to compare different metal mixing models. In particular, we examine the role of diffusion in the production of enriched outflows and in determining the metallicity distributions of gas and stars. We investigate different diffusion strengths by changing the pre-factor of the diffusion coefficient, by varying how the diffusion coefficient is calculated from the local velocity distribution, and by varying whether the speed of sound is included as a velocity term. Stronger diffusion produces a tighter [O/Fe]–[Fe/H] distribution in the gas and cuts off the gas metallicity distribution function at lower metallicities. Diffusion suppresses the formation of low-metallicity stars, even with weak diffusion, and also strips metals from enriched outflows. This produces a remarkably tight correlation between “metal mass-loading” (mean metal outflow rate divided by mean metal production rate) and the strength of diffusion, even when the diffusion coefficient is calculated in different ways. The effectiveness of outflows at removing metals from dwarf galaxies and the metal distribution of the gas is thus dependent on the strength of diffusion. By contrast, we show that the metallicities of stars are not strongly dependent on the strength of diffusion, provided that some diffusion is present.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".