OBSERVATIONAL PROPERTIES OF THE METAL-POOR THICK DISK OF THE MILKY WAY AND INSIGHTS INTO ITS ORIGINS
Bibliographic record
Abstract
We have undertaken the study of the elemental abundances and kinematic properties of a metal-poor sample of candidate thick-disk stars selected from the Radial Velocity Experiment spectroscopic survey of bright stars to differentiate among the present scenarios of the formation of the thick disk. In this paper, we report on a sample of 214 red giant branch, 31 red clump/horizontal branch, and 74 main-sequence/sub-giant branch metal-poor stars, which serves to augment our previous sample of only giant stars. We find that the thick disk [α/Fe] ratios are enhanced and have little variation (<0.1 dex), in agreement with our previous study. The augmented sample further allows, for the first time, investigation of the gradients in the metal-poor thick disk. For stars with [Fe/H] < -1.2, the thick disk shows very small gradients, <0.03 ± 0.02 dex kpc −1 , in α-enhancement, while we find a +0.01 ± 0.04 dex kpc −1 radial gradient and a −0.09 ± 0.05 dex kpc −1 vertical gradient in iron abundance. In addition, we show that the peak of the distribution of orbital eccentricities for our sample agrees better with models in which the stars that comprise the thick disk were formed primarily in the Galaxy, with direct accretion of stars contributing little. Our results thus disfavor direct accretion of stars from dwarf galaxies into the thick disk as a major contributor to the thick-disk population, but cannot discriminate between alternative models for the thick disk, such as those that invoke high-redshift (gas-rich) mergers, heating of a pre-existing thin stellar disk by a minor merger, or efficient radial migration of stars.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".