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Record W1994986899 · doi:10.1117/12.552317

Predicting the aerodynamic performance of the Canadian Very Large Optical Telescope

2004· article· en· W1994986899 on OpenAlexaffabout
Joeleff Fitzsimmons, Jennifer Dunn, Glen Herriot, Laurent Jolıssaınt, Scott Roberts, Kevin Cooper, Mahmoud Mamou

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2004
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdaptive optics and wavefront sensing
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsComputational fluid dynamicsAerodynamicsWind tunnelTelescopeEnclosureMetreAerospace engineeringInstrumentation (computer programming)Computer scienceRemote sensingOpticsPhysicsGeologyAstronomyEngineering

Abstract

fetched live from OpenAlex

A variety of aerodynamic studies have been completed to assist in the development of an integrated model for the Thirty Meter Telescope. These studies investigated the characteristics of wind loading on the Canadian Very Large Optical Telescope (VLOT) and produced preliminary data for input into the VLOT integrated model. We describe the details of, and present the results from, the computational fluid dynamic (CFD) analyses and wind tunnel (WT) tests. The validity of the CFD results is assessed through correlation studies that compare the CFD and WT results. Through extensive comparison of the mean and RMS coefficients of pressure and the power spectral density plots of the pressures within the enclosure, excellent correlations between the experimental and computational results are shown.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.799
Threshold uncertainty score0.405

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.007
GPT teacher head0.202
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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations7
Published2004
Admission routes2
Has abstractyes

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Same venueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIESame topicAdaptive optics and wavefront sensingFrench-language works237,207