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Low Frequency Science with the Square Kilometre Array

2002· article· en· W850275 on OpenAlexaff
A. R. Taylor

Bibliographic record

VenueSymposium - International Astronomical Union · 2002
Typearticle
Languageen
FieldPhysics and Astronomy
TopicRadio Astronomy Observations and Technology
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsPhysicsReionizationRedshiftAstronomyGalaxyRadio telescopeAstrophysicsTelescopeStarsSpectral resolutionRadio astronomyUniverseGalactic astronomyAngular resolution (graph drawing)Star formationMilky WaySpectral line

Abstract

fetched live from OpenAlex

Over the past several years an international community of scientists and engineers has emerged with a common goal to solve the technical challenge required to construct a giant radio telescope with a collecting area of one square kilometre. The Square Kilometre Array (SKA) will have a hundred times more collecting area than our most powerful existing radio telescopes, providing sensitivity of a few tens of nanoJy in the centimetre/decimetre wavelength continuum. With a spatial resolution better than the Hubble Space Telescope, a field of view larger than the full moon, and the ability to simultaneously image a wide range of red shift, the SKA will be the worlds premier spectroscopic imaging telescope at any waveband. At long wavelengths the SKA will be able to detect emission from atomic hydrogen gas at extreme redshifts, allowing study of the “Dark Ages” of the Universe, before, and during, the transition phase when the initial stars formed and reionization occurred. The combination of sensitivity, wide field of view and high angular resolution, will allow high resolution imaging of the interstellar media and magnetic field of a vast number of galaxies to high redshift. Measurements of atomic hydrogen emission and continuum emission will trace the star formation history of the Universe from primordial galaxies to the present.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.366
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.006
GPT teacher head0.201
Teacher spread0.195 · 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.

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

Citations0
Published2002
Admission routes1
Has abstractyes

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