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Record W2763528992 · doi:10.1088/1538-3873/aa8800

CCDLAB: A Graphical User Interface FITS Image Data Reducer, Viewer, and Canadian UVIT Data Pipeline

2017· article· en· W2763528992 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenuePublications of the Astronomical Society of the Pacific · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSolar and Space Plasma Dynamics
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsPipeline (software)ReducerComputer scienceInterface (matter)SatelliteData reductionRemote sensingReal-time computingEnvironmental scienceEngineeringAerospace engineeringGeologyOperating systemData miningCivil engineering

Abstract

fetched live from OpenAlex

CCDLAB was originally developed as a FITS image data reducer and viewer, and development was then continued to provide ground support for the development of the UVIT detector system provided by the Canadian Space Agency to the Indian Space Research Organization's ASTROSAT satellite and UVIT telescopes. After the launch of ASTROSAT and during UVIT's first-light and PV phase starting in 2015 December, necessity required the development of a data pipeline to produce scientific images out of the Level 1 format data produced for UVIT by ISRO. Given the previous development of CCDLAB for UVIT ground support, the author provided a pipeline for the new Level 1 format data to be run through CCDLAB with the additional satellite-dependent reduction operations required to produce scientific data. Features of the pipeline are discussed with focus on the relevant data-reduction challenges intrinsic to UVIT data.

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.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.113
Threshold uncertainty score0.995

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 studies0.0010.001
Scholarly communication0.0000.001
Open science0.0050.003
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 head0.029
GPT teacher head0.280
Teacher spread0.251 · 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