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Record W4401934436 · doi:10.1117/12.3019178

NFIRAOS project update: preparing for construction

2024· article· en· W4401934436 on OpenAlexaffabout
Jenny Atwood, Peter Byrnes, Jeffrey Crane, Joeleff Fitzsimmons, Glen Herriot, Krzysztof Caputa, Dan Kerley, Jordan Lothrop, Samuel Barrett, Katherine Silversides, Malcolm J. Smith, Ivan Wevers, Erning Zhao, A. Densmore, Kate Jackson, Olivier Lardière, Jean‐Pierre Véran, Jennifer Dunn, Corinne Boyer, Lianqi Wang, Emma Rautio-Roe, Margaret Hemphill

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdaptive optics and wavefront sensing
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsAdaptive opticsComputer scienceEnclosureScope (computer science)TelescopeFirst lightCalibrationOpticsAperture (computer memory)Remote sensingSystems engineeringEngineeringPhysicsMechanical engineeringLight sourceTelecommunicationsGeology

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). NFIRAOS supports three Near Infrared (NIR) client instruments, and provides exceptional image quality across the 2 arcminute field of view. In 2018, NFIRAOS passed the Final Design Review (FDR), but there have been several substantial changes more recently. The optical enclosure (ENCL) refrigeration design was updated for CO2 refrigerant, and an enclosure wall panel was prototyped. The polar-coordinate CCD for the laser guide star (LGS) wavefront sensor camera was replaced with a commercial C-Blue camera from First Light Imaging. More recently, the NFIRAOS Science Calibration Unit (NSCU), which was previously a separate Canadian contribution, was incorporated into the scope of NFIRAOS, and has progressed to the Conceptual Design level. In addition to these changes, the team has been working to bring the last of the low-risk subsystems to final design level in preparation for the beginning of construction.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.932
Threshold uncertainty score0.244

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 head0.016
GPT teacher head0.279
Teacher spread0.263 · 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 designTheoretical or conceptual
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

Citations4
Published2024
Admission routes2
Has abstractyes

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