Bibliographic record
Abstract
We derive stellar population parameters and radial gradients within 0.65 $R_e$ for spatially resolved spectra of 2968 early-type galaxies from MaNGA, spanning stellar velocity dispersions ($σ$) of 50--340 km s$^{-1}$. Light-weighted mean age and C, N, Na, Mg, and Fe abundances are obtained by inverting metallicity-composite stellar population models with isochrones that respond in $T_{\mathrm{eff}}$ to individual element abundance changes. Globally, $\log$(age) increases ($\sim$0.53 dex per decade), [Fe/H] declines slightly ($\sim$-0.06 dex per decade), and [X/Fe] for light elements rises (0.19--0.37 dex per decade for $\logσ< 2.0$ but steepens to nearly double slope for $\logσ> 2.0$) with $\logσ$. [Fe/H] peaks at $\logσ\sim 2.0$ and falls on either side. Light-element [X/Fe] anticorrelates with [Fe/H] ($\sim$-0.1 dex per decade). Astrophysical scatter is largest in low-$σ$ galaxies, especially for Fe and N. Internally, age gradients are nearly flat in low-$σ$ galaxies and slightly negative in high-$σ$ systems ($\sim$-0.04 dex per decade). [Fe/H] radial gradients steepen from -0.06 dex per decade to -0.15 dex per decade across $σ$, while light elements (except Na) show $\sim$-0.03 dex per decade gradients. Scatter in gradients peaks in high-$σ$ galaxies, most strongly for Fe ($\sim$0.23 dex), suggesting comparable numbers of inside-out and outside-in formation. A near-zero ($\sim$-0.03) age and light-element gradient plus mild [Fe/H] gradients supports hierarchical merging for ETG evolution. Simulations match the observed age structure, slope change at $\logσ\sim 2.0$, and flat gradients, though they overpredict absolute abundances.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| 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".