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Record W3006209120 · doi:10.3847/1538-3881/ab9ab9

The Sixth Data Release of the Radial Velocity Experiment (RAVE). I. Survey Description, Spectra, and Radial Velocities

2020· article· en· W3006209120 on OpenAlexafffund

Bibliographic record

VenueThe Astronomical Journal · 2020
Typearticle
Languageen
FieldPhysics and Astronomy
TopicStellar, planetary, and galactic studies
Canadian institutionsUniversity of Victoria
FundersKavli Institute for Theoretical Physics, University of California, Santa BarbaraScience and Technology Facilities CouncilNatural Sciences and Engineering Research Council of CanadaLeibniz-Institut für Astrophysik PotsdamIstituto Nazionale di AstrofisicaCentre National de la Recherche ScientifiqueAustralian Astronomical Optics-MacquarieDeutsche ForschungsgemeinschaftJavna Agencija za Raziskovalno Dejavnost RSMacquarie UniversityJohns Hopkins UniversityW. M. Keck FoundationAgence Nationale de la RechercheNational Science FoundationEuropean Space AgencyLeibniz-GemeinschaftCentre National d’Etudes SpatialesSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungInstitut National de Physique Nucléaire et de Physique des ParticulesVetenskapsrådet
KeywordsRadial velocityStarsSpectral lineData reductionStellar classificationAstronomical spectroscopy

Abstract

fetched live from OpenAlex

Abstract The Radial Velocity Experiment (R ave ) is a magnitude-limited (9 < I < 12) spectroscopic survey of Galactic stars randomly selected in Earth’s southern hemisphere. The R ave medium-resolution spectra ( R ∼ 7500) cover the Ca-triplet region (8410–8795 Å). The sixth and final data release (DR6) is based on 518,387 observations of 451,783 unique stars. R ave observations were taken between 2003 April 12 and 2013 April 4. Here we present the genesis, setup, and data reduction of R ave as well as wavelength-calibrated and flux-normalized spectra and error spectra for all observations in R ave DR6. Furthermore, we present derived spectral classification and radial velocities for the R ave targets, complemented by cross-matches with Gaia DR2 and other relevant catalogs. A comparison between internal error estimates, variances derived from stars with more than one observing epoch, and a comparison with radial velocities of Gaia DR2 reveals consistently that 68% of the objects have a velocity accuracy better than 1.4 km s –1 , while 95% of the objects have radial velocities better than 4.0 km s –1 . Stellar atmospheric parameters, abundances and distances are presented in a subsequent publication. The data can be accessed via the R ave website ( http://rave-survey.org ) or the Vizier database.

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 score0.020
Threshold uncertainty score0.538

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.044
GPT teacher head0.241
Teacher spread0.197 · 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

Citations161
Published2020
Admission routes2
Has abstractyes

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