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Record W2614782001 · doi:10.1051/0004-6361/201730747

The third data release of the Kilo-Degree Survey and associated data products

2017· article· en· W2614782001 on OpenAlexaff
J. T. A. de Jong, G. Verdoes Kleijn, T. Erben, H. Hildebrandt, Konrad Kuijken, G. Sikkema, M. Brescia, Maciej Bilicki, N. R. Napolitano, Valeria Amaro, K. Begeman, Danny Boxhoorn, Hugo Buddelmeijer, S. Cavuoti, F. Getman, A. Grado, Ewout Helmich, Zhuoyi Huang, N. Irisarri, F. La Barbera, G. Longo, John McFarland, Reiko Nakajima, M. Paolillo, E. Puddu, M. Radovich, A. Rifatto, C. Tortora, E. A. Valentijn, Civita Vellucci, Willem-Jan Vriend, A. Amon, Chris Blake, Ian Fenech Conti, Stephen Gwyn, Ricardo Herbonnet, Catherine Heymans, Henk Hoekstra, Dominik Klaes, Julian Merten, L. Miller, Peter Schneider, Massimo Viola

Bibliographic record

VenueAstronomy and Astrophysics · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGalaxies: Formation, Evolution, Phenomena
Canadian institutionsHerzberg Institute of Astrophysics
FundersDeutsche ForschungsgemeinschaftAustralian Astronomical Optics-MacquarieUniversità ta' MaltaAustralian Research CouncilAlexander von Humboldt-StiftungNederlandse Organisatie voor Wetenschappelijk OnderzoekINAF-Osservatorio Astronomico di PadovaBundesministerium für Wirtschaft und EnergieUniversità degli Studi di PadovaScience and Technology Facilities CouncilEuropean Regional Development FundEuropean Commission
KeywordsWeak gravitational lensingRedshiftQuasarPhysicsAstrophysicsGalaxyRedshift surveyContext (archaeology)AstronomyDegree (music)Photometric redshiftMilky WayGeographyArchaeology

Abstract

fetched live from OpenAlex

Context. The Kilo-Degree Survey (KiDS) is an ongoing optical wide-field imaging survey with the OmegaCAM camera at the VLT Survey Telescope. It aims to image 1500 square degrees in four filters (ugri). The core science driver is mapping the large-scale matter distribution in the Universe, using weak lensing shear and photometric redshift measurements. Further science cases include galaxy evolution, Milky Way structure, detection of high-redshift clusters, and finding rare sources such as strong lenses and quasars.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.051
Threshold uncertainty score0.172

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.009
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0510.058

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 head0.040
GPT teacher head0.242
Teacher spread0.203 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreDataset

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

Citations221
Published2017
Admission routes1
Has abstractyes

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