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Record W3111975192 · doi:10.1117/12.2560757

The InfraRed Imaging Spectrograph (IRIS) for TMT: support structure final design

2020· article· en· W3111975192 on OpenAlexaff
Brian Hoff, Joeleff Fitzsimmons, Gordon Lacy, Jennifer Dunn, Glen Herriot, Jeffrey Crane, Peter Byrnes, Dean Chalmers, David Andersen, Jenny Atwood, Tim Hardy

Bibliographic record

VenueAdvances in Optical and Mechanical Technologies for Telescopes and Instrumentation IV · 2020
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstronomy and Astrophysical Research
Canadian institutionsHerzberg Institute of Astrophysics
Fundersnot available
KeywordsSpectrographIRIS (biosensor)VibrationComputer scienceStiffnessMechanical engineeringStructural engineeringMaterials scienceEngineeringAcousticsPhysics

Abstract

fetched live from OpenAlex

The Support Structure for the Thirty Meter Telescope (TMT) Infrared Imaging Spectrograph (IRIS) consists of 18 carbonfiber reinforced polymer (CFRP) struts, a CFRP ring and a metal interface frame. This ultra-stiff, lightweight structure suspends the five-ton IRIS Science Cryostat and Rotator below the Narrow Field Infrared Adaptive Optics System (NFIRAOS). Through comprehensive design and analysis driven by requirements for stiffness, optical alignment, adjustability, manufacturability, weight and space, much headway was made to bring this design to fruition. This work presents the current state of design, including material down-selection, adjuster design and strategies for fabrication, alignment and testing. It summarizes methodologies and simulation results examining stiffness, seismic and thermal loads and transmission of vibration between NFIRAOS and IRIS. A prototype strut is being developed and will undergo dynamic mechanical testing to characterize its performance.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.023
Threshold uncertainty score0.076

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0230.013

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 head0.017
GPT teacher head0.292
Teacher spread0.276 · 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 designNot applicable
Domainnot available
GenreMethods

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

Citations1
Published2020
Admission routes1
Has abstractyes

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