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Record W4399732361 · doi:10.1117/12.3020167

The CASTOR mission: performance, uniqueness and science programs

2024· article· en· W4399732361 on OpenAlexaffabout
Melissa Amenouche, Deborah Lokhorst, Patrick Côté

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSpacecraft Design and Technology
Canadian institutionsHerzberg Institute of Astrophysics
Fundersnot available
KeywordsUniquenessComputer scienceMathematics

Abstract

fetched live from OpenAlex

CASTOR, for the Cosmological Advanced Survey Telescope for Optical and Ultraviolet Research, is a widefield space telescope that is under active development by the Canadian Space Agency (CSA). This 1m telescope will produce panoramic imaging of the UV/optical (150-550 nm) sky delivering HST-like image quality over a wide field of view (0.25 sq. deg.), in three filters simultaneously. CASTOR will be optimized for wide-field surveys, although the telescope may also feature low- and medium-resolution spectroscopic capabilities. The paper will describe CASTOR’s unique capabilities within the astronomical landscape in the coming decade, and present highlights from a recently completed Phase 0 study that defined the science mission, including 14 “Legacy Surveys” that span a wide range of fields: including Dark Energy and Weak Lensing; Time Domain and Multi-messenger Astronomy; Galaxy Evolution and AGNs; Star Formation and more.

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)0.002
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: Other · Consensus signal: none
Teacher disagreement score0.113
Threshold uncertainty score0.226

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.003

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 head0.010
GPT teacher head0.219
Teacher spread0.209 · 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
GenreOther

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 routes2
Has abstractyes

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Same topicSpacecraft Design and TechnologyFrench-language works237,207