Chemical Cartography with APOGEE: Two-process Parameters and Residual Abundances for 288,789 Stars from Data Release 17
Bibliographic record
Abstract
Abstract Stellar abundance measurements are subject to systematic errors that induce extra scatter and artificial correlations in elemental abundance patterns. We derive empirical calibration offsets to remove systematic trends with surface gravity log ( g ) in 17 elemental abundances of 288,789 evolved stars from the SDSS APOGEE survey. We fit these corrected abundances as the sum of a prompt process tracing core-collapse supernovae and a delayed process tracing Type Ia supernovae, thus recasting each star’s measurements into the amplitudes A cc and A Ia and the element-by-element residuals from this two-parameter fit. As a first application of this catalog, which is 8× larger than that of previous analyses that used a restricted log ( g ) range, we examine the median residual abundances of 14 open clusters, nine globular clusters, and four dwarf satellite galaxies. Relative to field Milky Way disk stars, the open clusters younger than 2 Gyr show ≈0.1−0.2 dex enhancements of the neutron-capture element Ce, and the two clusters younger than 0.5 Gyr also show elevated levels of C+N, Na, S, and Cu. Globular clusters show elevated median abundances of C+N, Na, Al, and Ce, and correlated abundance residuals that follow previously known trends. The four dwarf satellites show similar residual abundance patterns despite their different star formation histories, with ≈0.2–0.3 dex depletions in C+N, Na, and Al and ≈0.1 dex depletions in Ni, V, Mn, and Co. We provide our catalog of corrected APOGEE abundances, two-process amplitudes, and residual abundances, which will be valuable for future studies of abundance patterns in different stellar populations and of additional enrichment processes that affect galactic chemical evolution.
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 | 0.002 | 0.002 |
| 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) | 0.007 | 0.005 |
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".