The Chemo-Dynamical History of the Milky Way as Revealed by SDSS/SEGUE
Bibliographic record
Abstract
Abstract Although originally conceived as primarily an extragalactic survey, the Sloan Digital Sky Survey (SDSS-I), and its extensions SDSS-II and SDSS-III, continue to have a major impact on our understanding of the formation and evolution of our host galaxy, the Milky Way. The sub-survey SEGUE: Sloan Extension for Galactic Exploration and Understanding, excuted as part of SDSS-II, obtained some 3500 square degrees of additional ugriz imaging, mostly at lower Galactic latitudes, in order to better sample the disk systems of the Galaxy. Most importantly, it obtained over 240,000 medium-resolution spectra for stars selected to sample Galactocentric distances from 0.5 to 100 kpc. In combination with stellar targets from SDSS-I, and the recently completed SEGUE-2 program, executed as part of SDSS-III, the total sample of SDSS spectroscopy for Galactic stars comprises some 500,000 objects. The development of the SEGUE Stellar Parameter Pipeline has enabled the determination of accurate atmospheric parameter estimates for a large fraction of these stars. Many of the stars in this data set within 5 kpc of the Sun have sufficiently well-measured proper motions to determine their full space motions, permitting examination of the nature of much more distant populations represented by members that are presently passing through the solar neighborhood. Ongoing analyses of these data are being used to draw a much clearer picture of the nature of our galaxy, and to supply targets for detailed high-resolution spectrscopic follow-up with the world's largest telescopes. Here we discuss a few highlights of recently completed and ongoing investigations with these data.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| 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) | 0.001 | 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".