Unveiling the time evolution of chemical abundances across the Milky Way disk with APOGEE
Bibliographic record
Abstract
Chemical abundances are an essential tool in untangling the Milky Way's enrichment history. However, the evolution of the interstellar medium abundance gradient with cosmic time is lost as a result of radial mixing processes. For the first time, we quantify the evolution of many observational abundances across the Galactic disk as a function of lookback time and birth radius, $R_\text{birth}$. Using an empirical approach, we derive $R_\text{birth}$ estimates for 145,447 APOGEE DR17 red giant disk stars, based solely on their ages and [Fe/H]. We explore the detailed evolution of 6 abundances (Mg, Ca ($α$), Mn (iron-peak), Al, C (light), Ce (s-process)) across the Milky Way disk using 87,426 APOGEE DR17 red giant stars. We discover that the interstellar medium had three fluctuations in the metallicity gradient $\sim 9$, $\sim 6$, and $\sim4$ Gyr ago. The first coincides with the end of high-$α$ sequence formation around the time of the Gaia-Sausage-Enceladus disruption, while the others are likely related to passages of the Sagittarius dwarf galaxy. A clear distinction is found between present-day observed radial gradients with age and the evolution with lookback time for both [X/Fe] and [X/H], resulting from the significant flattening and inversion in old populations due to radial migration. We find the [Fe/H]--[$α$/Fe] bimodality is also seen as a separation in the $R_\text{birth}$--[X/Fe] plane for the light and $α$-elements. Our results recover the chemical enrichment of the Galactic disk over the past 12 Gyr, providing tight constraints on Galactic disk chemical evolution models.
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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 | 0.001 | 0.001 |
| 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) | 0.000 | 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".