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Record W4409160652 · doi:10.32920/28732928

Accuracy of spot color reproduction in a 7-color, expanded gamut, flexographic printing system

2025· preprint· en· W4409160652 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

Venuenot available
Typepreprint
Languageen
FieldPhysics and Astronomy
TopicColor Science and Applications
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsGamutComputer scienceComputer visionComputer graphics (images)Artificial intelligence

Abstract

fetched live from OpenAlex

<p>Expanded gamut printing involves expanding the number of process colors (cyan, magenta, yellow and black) by orange, green and violet colors to reproduce many spot colors with the new fixed ink set (CMYKOGV). This study focuses on evaluating flexographic expanded gamut printing on a narrow web flexographic label press located at Toronto Metropolitan University. Esko Equinox and GMG OpenColor expanded gamut software solutions were used, where each system was tested with its own proprietary characterization test chart. The Idealliance Expanded Color Gamut (ECG) test target was also used in this study. A verification test chart was created, with selected Pantone spot colors. The test chart was then processed using the characterization data from the proprietary and nonproprietary characterization press runs. The builds of the selected Pantone colors were analyzed and CIEDE2000 color difference was calculated. Both software solutions did better in regards to color accuracy with their proprietary characterization targets than using the data gathered from the Idealliance ECG small chart. </p>

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)0.001
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.513
Threshold uncertainty score0.804

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0000.001
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 head0.020
GPT teacher head0.301
Teacher spread0.282 · 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

Quick stats

Citations1
Published2025
Admission routes2
Has abstractyes

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