Metallicity Calibrations and the Mass‐Metallicity Relation for Star‐forming Galaxies
Bibliographic record
Abstract
We investigate the effect of metallicity calibrations, AGN classification, and aperture covering fraction on the local mass-metallicity ( M - Z ) relation using 27,730 star-forming galaxies from the SDSS Data Release 4. We analyze the SDSS M - Z relation with 10 metallicity calibrations, including theoretical and empirical methods. We show that the choice of metallicity calibration has a significant effect on the shape and y -intercept [12 + log (O/H) ] of the M - Z relation. The absolute metallicity scale ( y -intercept) varies up to Δ [ log (O/H) ] = 0.7 dex, depending on the calibration used, and the change in shape is substantial. These results indicate that it is critical to use the same metallicity calibration when comparing different luminosity-metallicity or M - Z relations. We present new metallicity conversions that allow metallicities that have been derived using different strong-line calibrations to be converted to the same base calibration. These conversions facilitate comparisons between different samples, particularly comparisons between galaxies at different redshifts for which different suites of emission lines are available. Our new conversions successfully remove the large 0.7 dex discrepancies between the metallicity calibrations, and we reach agreement in the M - Z relation to within 0.03 dex on average. We investigate the effect of AGN classification and aperture covering fraction on the M - Z relation. We find that different AGN classification methods have negligible effect on the SDSS M - Z relation. We compare the SDSS M - Z relation with nuclear and global relations from the NFGS. The turnover of the M - Z relation at M * ∼ 10 10 M ☉ depends on the aperture covering fraction. We find that a lower redshift limit of z < 0.04 is insufficient for avoiding aperture effects in fiber spectra of the highest stellar mass ( M * > 10 10 M ☉ ) galaxies.
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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 | 0.002 | 0.009 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".