ABYSS. I. Targeting Strategy for the APOGEE and BOSS Young Star Survey in SDSS-V
Bibliographic record
Abstract
Abstract The fifth iteration of the Sloan Digital Sky Survey is set to obtain optical and near-infrared spectra of ∼5 million stars of all ages and masses throughout the Milky Way. As a part of these efforts, APOGEE and BOSS Young Star Survey (ABYSS) will observe ∼105 stars with ages <30 Myr that have been selected using a set of homogeneous selection functions that make use of different tracers of youth. The ABYSS targeting strategy we describe in this paper is aimed to provide the largest spectroscopic census of young stars to date. It consists of eight different types of selection criteria that take the position on the H-R diagram, infrared excess, variability, as well as the position in phase space in consideration. The resulting catalog of ∼200,000 sources (of which a half are expected to be observed) provides representative coverage of the young Galaxy, including both nearby diffuse associations as well as more distant massive complexes, reaching toward the inner Galaxy and the Galactic center.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.023 | 0.031 |
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".