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Record W2055535946 · doi:10.1002/col.10049

A data set for color research

2002· article· en· W2055535946 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

VenueColor Research & Application · 2002
Typearticle
Languageen
FieldPhysics and Astronomy
TopicColor Science and Applications
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsData setComputer scienceSet (abstract data type)Context (archaeology)Process (computing)Experimental dataData collectionArtificial intelligenceComputer visionMathematicsGeography

Abstract

fetched live from OpenAlex

Abstract We present an extensive data set for color research that has been made available online ( www.cs. sfu.ca/∼colour/data) . The data are especially germane to research into computational color constancy, but we have also aimed to make the data as general as possible, and we anticipate a wide range of benefits to research into computational color science and computer vision. Because data are useful only in context, we provide the details of the collection process, including the camera characterization, and the data used to determine that characterization. The most significant part of the data is 743 images of scenes taken under a carefully chosen set of 11 illuminants. The data set also has several standardized sets of spectra for synthetic data experiments, including some data for fluorescent surfaces. © 2002 Wiley Periodicals, Inc. Col Res Appl, 27, 147–151, 2002; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/col.10049

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.240
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.002

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 head0.616
GPT teacher head0.561
Teacher spread0.055 · 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