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Record W2003632067 · doi:10.1088/1757-899x/44/1/012019

GALFACTS: The galactic ALFA continuum transit survey

2013· article· en· W2003632067 on OpenAlexaff
A. R. Taylor

Bibliographic record

VenueIOP Conference Series Materials Science and Engineering · 2013
Typearticle
Languageen
FieldPhysics and Astronomy
TopicRadio Astronomy Observations and Technology
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsSkyRadio telescopePhysicsTelescopeAstronomyImaging spectrometerAngular resolution (graph drawing)Polarization (electrochemistry)Radio astronomyGalaxySpectrometerSpectral resolutionRemote sensingOpticsGeologySpectral line

Abstract

fetched live from OpenAlex

The Arecibo Radio Telescope has been equipped with a seven feed horn array operating at 1400 MHz – the Arecibo L-band Feed Array (ALFA). This new feed system, combined with a 300 MHz digital spectrometer back end, enables the large collecting area of the Arecibo reflector to be utilized for sensitive, wide-area imaging. A consortium of over 40 researchers from around the world is working together to use this system to carry out a spectro-polarimetric imaging survey of the entire sky visible to the Arecibo Telescope; the Galactic ALFA Continuum Transit Survey (GALFACTS). GALFACTS covers 13,000 square degrees of sky, creating over this area full-Stokes image cubes at angular resolution of 3.5' with several thousand spectral channels covering 1225 to 1525 MHz, and band-averaged sensitivity of 90 microJy. The complete survey will require about 2000 hours of observing time. Observations began in l2009 and are now 80% complete. The data will provide a rich new database for exploration of the magnetic field of the Galaxy, the properties of the magneto-ionic medium and the polarization properties of over 10 5 extragalactic radio sources.

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)0.000
metaresearch head score (Gemma)0.000
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: Empirical
Teacher disagreement score0.031
Threshold uncertainty score0.061

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.003

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 head0.014
GPT teacher head0.196
Teacher spread0.182 · 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

Citations3
Published2013
Admission routes1
Has abstractyes

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