THE OPEN CLUSTER CHEMICAL ANALYSIS AND MAPPING SURVEY: LOCAL GALACTIC METALLICITY GRADIENT WITH APOGEE USING SDSS DR10
Bibliographic record
Abstract
The Open Cluster Chemical Analysis and Mapping (OCCAM) survey aims to produce a comprehensive, uniform, infrared-based data set for hundreds of open clusters, and constrain key Galactic dynamical and chemical parameters from this sample. This first contribution from the OCCAM survey presents analysis of 141 members stars in 28 open clusters with high-resolution metallicities derived from a large uniform sample collected as part of the Sloan Digital Sky Survey III/Apache Point Observatory Galactic Evolution Experiment. This sample includes the first high-resolution metallicity measurements for 22 open clusters. With this largest ever uniformly observed sample of open cluster stars we investigate the Galactic disk gradients of both [M/H] and [α/M]. We find basically no gradient in [α/M] across 7.9 kpc ⩽ R GC ⩽ 14.5 kpc, but [M/H] does show a gradient for R GC < 10 kpc and a significant flattening beyond R GC = 10 kpc. In particular, whereas fitting a single linear trend yields an [M/H] gradient of −0.09 ± 0.03 dex kpc −1 —similar to previously measure gradients inside 13 kpc—by independently fitting inside and outside 10 kpc separately we find a significantly steeper gradient near the Sun (7.9 ⩽ R GC ⩽ 10) than previously found (−0.20 ± 0.08 dex kpc −1 ) and a nearly flat trend beyond 10 kpc (−0.02 ± 0.09 dex kpc −1 ).
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".