DETERMINING AGES OF APOGEE GIANTS WITH KNOWN DISTANCES - Full PDFs
Bibliographic record
Abstract
Supplementary data to Feuillet+ (2016, ApJ, arXiv:1511.04088):\n\nUsing the APOGEE survey instrument and the NMSU 1m telescope, we observe a sample of bright, nearby, red giant stars with known distances measured by Hipparcos. By applying Bayesian analysis and hierarchical modeling, we determine individual stellar ages and the star formation histories of single alpha-abundance subsamples. The probability distribution functions (PDFs) and modeled star formation histories (SFHs) of all stars analyzed in this paper.\n\nData included: APOGEE/2MASS ID, the age values for PDFs in log(age), the isochrone matching likelihood function as given in Equation 4 of paper, the age PDF of a Bayesian analysis using a flat SFH (Section 4.3), the hierarchically modeled SFH using an alpha-abundance dependent Gaussian+uniform model (Section 4.6), and the age PDF of an empirical Bayesian analysis using the hierarchically modeled SFH (Section 4.6). Individual ages of stars in the paper are taken as the mean of the age PDF, however, most PDFs are non-Gaussian, which introduces complicated errors into the selection of a single age.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.019 |
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".