Stellar mass, not dynamical mass nor gravitational potential, drives the mass-metallicity relationship
Bibliographic record
Abstract
The widely known relation between stellar mass and gas metallicity (mass-metallicity relation, MZR) in galaxies is often ascribed to the higher capability of more massive systems to retain metals against the action of galactic outflows. In this scenario the stellar mass would simply be an indirect proxy of the dynamical mass or of the gravitational potential. We test this scenario by using a sample of more than one thousand star-forming galaxies from the MaNGA survey for which dynamical masses have been accurately determined. By using three different methods (average dispersion, Partial Correlation Coefficients, Random Forest) we unambiguously find that the gas metallicity depends primarily and fundamentally on the stellar mass. Once the dependence on stellar mass is taken into account, there is little or no dependence on either dynamical mass or gravitational potential (and, if anything, the metallicity dependence on the latter quantities is inverted). Our result indicates that the MZR is not caused by the retention of metals in more massive galaxies. The direct, fundamental dependence of metallicity on stellar mass suggests the much simpler scenario in which the MZR is just a consequence of the stellar mass being proportional to the integral of metals production in the galaxy.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".