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Record W2015412178 · doi:10.1088/0004-637x/694/2/1610

THE YUAN-TSEH LEE ARRAY FOR MICROWAVE BACKGROUND ANISOTROPY

2009· article· en· W2015412178 on OpenAlexaff

Bibliographic record

VenueThe Astrophysical Journal · 2009
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGalaxies: Formation, Evolution, Phenomena
Canadian institutionsCanadian Institute for Theoretical Astrophysics
FundersScience and Technology Facilities Council
KeywordsCosmic microwave backgroundSynchrotron radiationGalaxyCosmic background radiationInterferometryBackground radiationMicrowaveAnisotropySubaru Telescope

Abstract

fetched live from OpenAlex

The Yuan-Tseh Lee Array for microwave background anisotropy is the first interferometer dedicated to study the cosmic microwave background radiation at 3 mm wavelength. The choice of 3 mm is to minimize the contributions from foreground synchrotron radiation and Galactic dust emission. The initial configuration of seven 0.6 m telescopes mounted on a 6 m hexapod platform was dedicated in 2006 October on Mauna Loa, Hawaii. Scientific operations began with the detection of a number of clusters of galaxies via the thermal Sunyaev–Zel'dovich effect. We compare our data with Subaru weak-lensing data to study the structure of dark matter. We also compare our data with X-ray data to derive the Hubble constant.

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)0.002
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.048
Threshold uncertainty score0.161

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0480.017

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 head0.011
GPT teacher head0.233
Teacher spread0.222 · 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 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

Citations38
Published2009
Admission routes1
Has abstractyes

Explore more

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