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Record W4293282768 · doi:10.1117/12.2630715

Final integration of the Gemini High-Resolution Optical SpecTrograph (GHOST) spectrograph

2022· article· en· W4293282768 on OpenAlexaffabout
John Pazder, Alan W. McConnachie, Michael Ireland, André Anthony, John Bassett, Greg Burley, Edward L. Chapin, Vladimir Churilov, A. Densmore, Jennifer Dunn, Tony Farrell, David K. Henderson, Brian Hoff, Jordan Lothrop, Scott MacDonald, S. Margheim, В. А. Решетов, Ivan Wevers, Lewis Waller, P. Young, Ross Zhelem

Bibliographic record

VenueGround-based and Airborne Instrumentation for Astronomy IX · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstronomy and Astrophysical Research
Canadian institutionsHerzberg Institute of Astrophysics
Fundersnot available
KeywordsSpectrographResolution (logic)PhysicsOpticsComputer scienceAstronomyArtificial intelligenceSpectral line

Abstract

fetched live from OpenAlex

The Gemini High-Resolution Optical SpecTrograph (GHOST) instrument is the next generation high resolution spectrograph for the Gemini telescope. The GHOST instrument was developed for the Gemini telescope as a collaboration between Australian Astronomical Optics (AAO) at Macquarie University, the Herzberg Astronomy and Astrophysics (HAA) in Canada and the Australian National University (ANU). The instrument is a fiber fed spectrograph with R<50,000 in two-object mode and R<75,000 in single object mode. The bench spectrograph was integrated at Gemini South from April to June 2022. This paper presents the final integration and alignment of the spectrograph at Gemini South and the measured spectrograph performance at the telescope.

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)0.003
metaresearch head score (Gemma)0.002
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: none
Teacher disagreement score0.015
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.003

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 head0.015
GPT teacher head0.259
Teacher spread0.244 · 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

Citations3
Published2022
Admission routes2
Has abstractyes

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