{"id":"W4409218967","doi":"10.32920/28745420.v1","title":"Flexographic Expanded gamut printing with Proprietary and Nonproprietary Characterization Charts","year":2025,"lang":"en","type":"preprint","venue":"","topic":"Color Science and Applications","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Gamut; Characterization (materials science); Computer science; Computer graphics (images); Materials science; Artificial intelligence; Nanotechnology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001226385,0.0002530295,0.0002557195,0.0001293349,0.0002157446,0.0001677955,0.0002232836,0.00008275601,0.0001607716],"category_scores_gemma":[0.000001294111,0.0002003988,0.00006953112,0.0002746442,0.0001072696,0.0001436547,0.0004616528,0.0003616299,0.000006132069],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006319623,"about_ca_system_score_gemma":0.0001931293,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001536353,"about_ca_topic_score_gemma":0.00001234755,"domain_scores_codex":[0.9987083,0.0000201261,0.0002420254,0.000621031,0.000176912,0.0002315911],"domain_scores_gemma":[0.9991875,0.00001692613,0.0001680548,0.0004383178,0.0001079619,0.00008123626],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0000451555,0.000397553,0.725898,0.0004014552,0.0003236926,0.000002423566,0.001237517,0.0001324241,0.0122068,0.03357808,0.000105626,0.2256712],"study_design_scores_gemma":[0.001699652,0.0002164409,0.8972421,0.001550025,0.0005927724,0.000007419264,0.001224777,0.02568595,0.02723117,0.02947354,0.01210769,0.0029685],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9518664,0.00002001155,0.0336213,0.001032854,0.0001021565,0.001174881,0.0000689362,0.0001190155,0.01199437],"genre_scores_gemma":[0.9946501,0.0000168325,0.001924778,0.0001129201,0.0001658829,0.0004565046,0.0004314186,0.00001112816,0.002230448],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2227028,"threshold_uncertainty_score":0.8172027,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007988875025649753,"score_gpt":0.2348697064585515,"score_spread":0.2268808314329017,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}