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Record W4200501154 · doi:10.3847/1538-3881/ac2cbc

Final Targeting Strategy for the SDSS-IV APOGEE-2S Survey

2021· article· en· W4200501154 on OpenAlexafffund
Felipe A. Santana, Rachael L. Beaton, Kevin R. Covey, Julia O’Connell, Penélope Longa-Peña, Roger E. Cohen, José G. Fernández-Trincado, Christian R. Hayes, Gail Zasowski, Jennifer Sobeck, Steven R. Majewski, Nathan De Lee, Ryan J. Oelkers, Guy S. Stringfellow, Andrés Almeida, Borja Anguiano, John Donor, Peter M. Frinchaboy, Sten Hasselquist, Jennifer A. Johnson, Juna A. Kollmeier, David L. Nidever, Adrian M. Price-Whelan, Á. Rojas-Arriagada, M. Schultheis, Matthew Shetrone, Joshua D. Simon, C. Aerts, J. Borissova, M. R. Drout, D. Geisler, Chi-Yan Law, N. Medina, D. Minniti, Antonela Monachesi, Ricardo R. Muñoz, R. Poleski, Alexandre Roman–Lopes, Kevin C. Schlaufman, Amelia M. Stutz, Johanna Teske, A. Tkachenko, Jennifer L. van Saders, Alycia J. Weinberger, M. Zoccali

Bibliographic record

VenueThe Astronomical Journal · 2021
Typearticle
Languageen
FieldPhysics and Astronomy
TopicStellar, planetary, and galactic studies
Canadian institutionsUniversity of Toronto
FundersCHIST-ERALawrence Berkeley National LaboratorySmithsonian Astrophysical ObservatoryJet Propulsion LaboratoryInstituto de Astrofísica de CanariasOffice of ScienceMax-Planck-Institut für AstronomieMax-Planck-Institut für AstrophysikAgencia Nacional de Investigación y DesarrolloUniversidad Nacional Autónoma de MéxicoMax-Planck-GesellschaftUniversity of Colorado BoulderKU LeuvenMinistério da Ciência, Tecnologia e InovaçãoEuropean CommissionUniversity of OxfordYork UniversityAgenția Națională pentru Cercetare și DezvoltareCarnegie Institution for ScienceBanco SantanderLeibniz-GemeinschaftUniversity of Notre DameCarnegie Mellon UniversityUniversity of WashingtonEuropean Space AgencyPrinceton UniversityAlfred P. Sloan FoundationJohns Hopkins UniversityCarnegie Institution of WashingtonUniversity of UtahOhio State UniversitySmithsonian InstitutionU.S. Department of EnergyCalifornia Institute of TechnologyNational Aeronautics and Space AdministrationNew Mexico State UniversityUniversity of California, Los AngelesUniversity of PortsmouthVanderbilt UniversityYale UniversityBelgian Federal Science Policy OfficeNational Science Foundation
KeywordsMilky WayComputer scienceGeologyPaleontologyStarsComputer vision

Abstract

fetched live from OpenAlex

Abstract APOGEE is a high-resolution ( R ∼ 22,000), near-infrared, multi-epoch, spectroscopic survey of the Milky Way. The second generation of the APOGEE project, APOGEE-2, includes an expansion of the survey to the Southern Hemisphere called APOGEE-2S. This expansion enabled APOGEE to perform a fully panoramic mapping of all of the main regions of the Milky Way; in particular, by operating in the H band, APOGEE is uniquely able to probe the dust-hidden inner regions of the Milky Way that are best accessed from the Southern Hemisphere. In this paper we present the targeting strategy of APOGEE-2S, with special attention to documenting modifications to the original, previously published plan. The motivation for these changes is explained as well as an assessment of their effectiveness in achieving their intended scientific objective. In anticipation of this being the last paper detailing APOGEE targeting, we present an accounting of all such information complete through the end of the APOGEE-2S project; this includes several main survey programs dedicated to exploration of major stellar populations and regions of the Milky Way, as well as a full list of programs contributing to the APOGEE database through allocations of observing time by the Chilean National Time Allocation Committee and the Carnegie Institution for Science. This work was presented along with a companion article, Beaton et al. (2021), presenting the final target selection strategy adopted for APOGEE-2 in the Northern Hemisphere.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.043
Threshold uncertainty score0.842

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.049
GPT teacher head0.273
Teacher spread0.224 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations97
Published2021
Admission routes2
Has abstractyes

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