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Record W4301185465 · doi:10.1117/12.2629665

HEART: Gemini Infrared Multi-Object Spectrograph (GIRMOS) Real-time Controller using Herzberg Extensible Adaptive Real-time Toolkit (HEART)

2022· article· en· W4301185465 on OpenAlexaff
Lianne Mueller, Jennifer Dunn, Dan Kerley, Edward L. Chapin, Malcolm J. Smith, Darryl Gamroth, Jonathan Stocks, Kathryn Jackson, Glen Herriot, Jean‐Pierre Véran

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Control Systems Optimization
Canadian institutionsHerzberg Institute of Astrophysics
Fundersnot available
KeywordsExtensibilityComputer scienceSpectrographObject (grammar)Real-time computingPhysicsProgramming languageArtificial intelligence

Abstract

fetched live from OpenAlex

This paper will discuss the Gemini Infrared Multi-Object Spectrograph (GIRMOS) with a focus on the design of its facility class Adaptive Optics (AO) Real Time Controller (RTC). The GIRMOS Adaptive Optics Real-Time Controller (GIRMOS RTC) will be developed using the Herzberg Extensible Adaptive Real-time Toolkit (HEART), a C/C++ software framework for constructing RTCs that targets general-purpose CPUs and standard networking hardware. The GIRMOS RTC just finished a successful pre-build phase where the custom parts of GIRMOS were designed and it was shown how the design incorporated HEART’s software modules. The GIRMOS RTC as a Multi-Object implementation of HEART will leverage a decade of design, modelling, and prototyping effort aimed to support the performance and configurability requirements of AO systems, with support for multiple client science instruments. This paper will discuss how HEART can be customized for a Multi-Object AO (MOAO) system.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.393
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.013
GPT teacher head0.230
Teacher spread0.217 · 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.

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

Citations1
Published2022
Admission routes1
Has abstractyes

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