The SAMI galaxy survey: exploring the gas-phase mass–metallicity relation
Bibliographic record
Abstract
We present a detailed exploration of the stellar mass versus gas-phase metallicity relation (MZR) using integral field spectroscopy data obtained from ∼1000 galaxies observed by the SAMI galaxy survey.These spatially resolved spectroscopic data allow us to determine the metallicity within the same physical scale (R eff ) for different calibrators.The shape of the MZ relations is very similar between the different calibrators, while there are large offsets in the absolute values of the abundances.We confirm our previous results derived using the spatially resolved data provided by the CALIFA and MaNGA surveys: (1) we do not find any significant secondary relation of the MZR with either the star formation rate (SFR) or the specific SFR (SFR/M * ) for any of the calibrators used in this study, based on the analysis of the individual residuals; (2) if there is a dependence with the SFR, it is weaker than the reported one (r c ∼ -0.3), it is confined to the low-mass regime (M * < 10 9 M ) or high-SFR regimes, and it does not produce any significant improvement in the description of the average population of galaxies.The aparent disagreement with published results based on single-fibre spectroscopic data could be due to (i) the interpretation of the secondary relation itself; (ii) the lower number of objects sampled at the low-mass regime by the current study; or (iii) the presence of extreme star-forming galaxies that drive the secondary relation in previous results.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".