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Record W4399665261 · doi:10.1117/12.3019334

NFIRAOS integration phase: planning for capacity, integration, and other logistics

2024· article· en· W4399665261 on OpenAlexfundno aff
Jenny Atwood, David R. Andersen, Glen Herriot, Peter Byrnes, Jean‐Pierre Véran, Jeffrey Crane

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicReliability and Maintenance Optimization
Canadian institutionsnot available
FundersOntario Ministry of Research and InnovationBritish Columbia Knowledge Development FundNatural Sciences and Engineering Research Council of CanadaNational Astronomical Observatory of JapanAssociation of Canadian Universities for Research in AstronomyNational Institutes of Natural SciencesCalifornia Institute of TechnologyGordon and Betty Moore FoundationNational Science Foundation
KeywordsPhase (matter)Capacity planningComputer scienceProcess managementBusinessOperating system

Abstract

fetched live from OpenAlex

NFIRAOS (Narrow-Field InfraRed Adaptive Optics System) will be the first-light multi-conjugate adaptive optics system for the Thirty Meter Telescope (TMT). The system will be built, tested, and integrated with the first instrument, IRIS (InfraRed Imaging Spectrograph), at Herzberg Astronomy and Astrophysics (HAA) in Victoria BC. NFIRAOS is a complex instrument that will require careful integration planning to meet cost, schedule and performance deliverables. HAA has purpose-built a new facility for the integration of NFIRAOS. We present the key features of this building, and their roles during the assembly, integration, and test phase (AIV). NFIRAOS and IRIS will be fully operational in Victoria, including providing calibration sources, and able to close the adaptive optics (AO) loops with the IRIS On-Instrument Wavefront sensors. NFIRAOS will then be disassembled and shipped to TMT for final construction and commissioning, which requires navigating some logistical challenges.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.962
Threshold uncertainty score0.269

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 head0.041
GPT teacher head0.293
Teacher spread0.252 · 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 designSimulation or modeling
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

Citations0
Published2024
Admission routes1
Has abstractyes

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