2025ApJS..281...27P

As the field of exoplanetary astronomy has matured, demand has grown for precise stellar abundances to probe subtle correlations between stellar compositions and planetary demographics. However, drawing population-level conclusions from the disparate measurements in the literature is challenging, with various groups measuring metallicities using bespoke codes with differing line lists, radiative transfer models, and other assumptions. Homogeneous analyses are thus critical. Here we use the neural-net framework uberMS to measure iron abundances and alpha enrichments from high-resolution optical spectra observed by the Tillinghast Reflector Echelle Spectrograph (TRES), a key resource used for the follow-up of candidate exoplanet hosts. To contextualize these measurements and benchmark our method's performance, we compare to external constraints on metallicity using the Hyades cluster, wide binaries, and asteroids, to external constraints on T_eff and ${\mathrm{log}}g$ using stars with interferometric radii, and to the results of other abundance measurement methods using overlap samples with the APOGEE and SPOCS catalogs, as well as by applying the Stellar Parameter Classification method directly to TRES spectra. We find that TRES-uberMS provides reliable parameter estimates with errors of roughly 100 K in T_eff, 0.09 dex in ${\mathrm{log}}g$, and 0.04 dex in [Fe/H] for many nearby dwarf stars, although [Fe/H] performance is poorer for mid-to-late K dwarfs, with the bias worsening with decreasing T_eff. Performance is also worse for evolved stars. For [α/Fe], our error may be as good as 0.03 dex for dwarfs based on external benchmarks, despite sizable and variable systematic differences when comparing with specific alpha-element abundances from other catalogs.