The dependence of the gradients of oxygen and nitrogen-to-oxygen on stellar age in MaNGA galaxies
Bibliographic record
Abstract
We derived the oxygen abundance (O/H), the nitrogen-to-oxygen (N/O) abundance ratio, and their corresponding radial gradients for a sample of 1431 galaxies from the MaNGA DR15 survey using two different realisations of the strong line method: empirical R calibration and the Bayesian model-based HII-CHI-MISTRY (HCM) code. We find that both abundance calculation methods reveal a correlation between the O/H gradient and the stellar mass of a galaxy. This relation is non-linear, with the steepest average gradients in the intermediate mass range and flatter average gradients for high- and low-mass galaxies. The relation between the N/O gradient and the stellar mass is, on average, non-linear with the steepest gradients in the intermediate mass range (log(M/M⊙)∼10), flatter gradients for high-mass galaxies, and the flattest gradients for low-mass galaxies. However, the general trend of steepening N/O gradients for higher masses, as reported in previous studies, remains evident. We find a dependence between the O/H and N/O gradients and the galaxy mean stellar age traced by the D(4000) index. For galaxies of lower masses, both gradients are, generally, steeper for intermediate values of D(4000) and flatter for low and high values of D(4000). Only the most massive galaxies do not show this correlation. We interpret this behaviour as an evolution of the metallicity gradients with the age of stellar population. Though the galaxies with a positive slope of the D(4000) radial gradient tend to have flatter O/H and N/O gradients, as compared to those with a negative D(4000) gradient.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".