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Record W2276356691 · doi:10.2312/egt.20021061

More than RGB: Spectral Trends in Color Reproduction

2002· article· en· W2276356691 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.

Bibliographic record

VenueEurographics · 2002
Typearticle
Languageen
FieldPhysics and Astronomy
TopicColor Science and Applications
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsRGB color modelComputer scienceArtificial intelligenceReproductionComputer visionSpectral colorSpectral analysisColor spaceColor modelPhysicsBiologyEcologyAstronomy

Abstract

fetched live from OpenAlex

Early rendering algorithms relied exclusively on three-dimensional spaces for color computation, such as RGB and CIE XYZ. Recent rendering advances use full spectral information for illuminants and surfaces, resulting in much greater accuracy and realism. These expensive computations can be wasted, however, if ad hoc methods are used to adjust the final image on the monitor, in film, or in print. Ineficiency and inaccuracy can be avoided with some knowledge of device gamuts and color reproduction algorithms. This course follows spectral data through the graphics pipeline, examining issues of rendering, color science, perception, gamut mapping, and color management. We conclude with a discussion of trends and open problems in managing spectral data for accurate color reproduction. Participants will learn not only the theoretical background of color and spectral reproduction, but practical guidelines often omitted in technical papers.

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 score0.163
Threshold uncertainty score0.446

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.025
GPT teacher head0.272
Teacher spread0.247 · 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