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Record W4285585958 · doi:10.1117/12.2629999

CCAT-prime: observatory control software for FYST

2022· article· en· W4285585958 on OpenAlexaffabout
R. Schaaf, Mike Nolta, R. Higgins, Thomas Nikola, R. Timmermann

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicParallel Computing and Optimization Techniques
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsComputer sciencePrime (order theory)SoftwareObservatoryProgramming languagePhysicsAstronomyMathematicsCombinatorics

Abstract

fetched live from OpenAlex

The Fred Young Submillimeter Telescope (FYST) is a 6-meter diameter telescope with a surface accuracy of 10 microns, operating at submillimeter to millimeter wavelengths. It will be located at 5600 meters elevation on Cerro Chajnantor in the Atacama desert of northern Chile overlooking the ALMA site. Its novel optical “crossed-Dragone” design will deliver a high-throughput, wide field-of-view telescope capable of mapping the sky very rapidly and efficiently. The telescope can host up to three instruments, with the heterodyne array “CHAI” and the direct-detection camera “Prime-Cam” as first-generation instruments. The often harsh environmental conditions at the telescope site require that FYST be operated remotely, either from the base station near San Pedro de Atacama or from the scientist’s home institutions in the US, Canada and Germany. Automated observations will therefore be the dominant observation mode. FYST’s Observatory Control System (OCS) gives instrument teams the responsibility to control observations. We believe that this model is a good fit for FYST because the observatory will operate exclusively in campaign mode. Furthermore, instrument teams have significant investments in software they want to preserve. The OCS adopts a micro-service design using off-theshelf components as far as possible to minimize development effort. We will present the OCS design and the selection of off-the-shelf components used.

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: Methods
Teacher disagreement score0.728
Threshold uncertainty score0.345

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 science0.0010.000
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 head0.022
GPT teacher head0.251
Teacher spread0.229 · 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
Published2022
Admission routes2
Has abstractyes

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