ML regressors trained on APOGEE DR17 red giants predict C, O, Mg, Si abundances from kinematics and [Fe/H] more accurately than [Fe/H] baseline, with external validation on HARPS FGK dwarfs and reproduction of Galactic chemical evolution trends.
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2026 2verdicts
UNVERDICTED 2representative citing papers
Elite-league ML models produce stable performance hierarchies, but the same models applied to university football show reordered key indicators, lower explanation stability, and weaker agreement across methods.
citing papers explorer
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Inferring stellar metallicity and elemental abundances from kinematic and spectroscopic data using machine learning -- Implications for exoplanet host stars
ML regressors trained on APOGEE DR17 red giants predict C, O, Mg, Si abundances from kinematics and [Fe/H] more accurately than [Fe/H] baseline, with external validation on HARPS FGK dwarfs and reproduction of Galactic chemical evolution trends.
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Interpretable Machine Learning for Football Performance Analysis: Evidence of Limited Transferability from Elite Leagues to University Competition
Elite-league ML models produce stable performance hierarchies, but the same models applied to university football show reordered key indicators, lower explanation stability, and weaker agreement across methods.