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Record W2287831074 · doi:10.14288/1.0063505

Design of weight-optimized space frame for the Canadian large adaptive reflector

2009· article· en· W2287831074 on OpenAlexaboutno aff
Ya-Ying Chang

Bibliographic record

VenuecIRcle (University of British Columbia) · 2009
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdaptive optics and wavefront sensing
Canadian institutionsnot available
Fundersnot available
KeywordsReflector (photography)Frame (networking)Space (punctuation)Computer scienceRemote sensingTelecommunicationsOpticsGeographyPhysics

Abstract

fetched live from OpenAlex

The Square-Kilometer Array (SKA) is an international project for building the next radio telescope. With a collecting area of one square-kilometer, the SKA will be 100 times more sensitive than current radio telescopes. The concepts for the SKA elements include nested phased arrays, large spherical reflectors, and many small parabolic antennas. The Large Adaptive Reflector (LAR) is the Canadian concept of building the SKA. The LAR is a long focal-length parabolic reflector which uses an airborne platform to support the focal receiver. The feed is held in plane by a tension-structure consisting of three or more tethers tensioned by the lift of a large helium-filled aerostat. The reflector is made up of segmented panels whose height and angle to zenith of segmented panels can be adjusted to focus on any point within zenith and azimuth angle coverage. Unlike conventional radio telescope, LAR is based on reflective optics thus the usable frequency range is limited by the surface accuracy of the reflector. Main structural components of the LAR are foundations, actuators (primary and secondary), main support structures, and reflector panels. This report includes investigation on the feasibility of using LAR antennas as elements to form the SKA, and the conceptual design of a triangular space frame, which is used as the main support structure.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.914
Threshold uncertainty score0.525

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.015
GPT teacher head0.195
Teacher spread0.181 · 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 designOther design
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
Published2009
Admission routes1
Has abstractyes

Explore more

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