{"id":"W2560216520","doi":"10.1117/1.jei.26.1.011014","title":"Chromatic modulation in visual art: a computational perspective","year":2016,"lang":"en","type":"article","venue":"Journal of Electronic Imaging","topic":"Aesthetic Perception and Analysis","field":"Neuroscience","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Hue; Palette (painting); Color space; Painting; Computer science; Perspective (graphical); Artificial intelligence; Computer vision; Chromatic scale; Color vision; Color image; Monochromatic color; Lightness; RGB color model; False color; Color balance; Focus (optics); Computer graphics (images); Art; Mathematics; Image processing; Image (mathematics); Visual arts; Optics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004619078,0.0003196257,0.000401727,0.001082809,0.0005960061,0.003389869,0.001021101,0.0006733493,0.003950852],"category_scores_gemma":[0.002595187,0.0003308257,0.001065062,0.0009257459,0.002966546,0.004151076,0.001305101,0.001002624,0.0002505138],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007690067,"about_ca_system_score_gemma":0.0004141246,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00230521,"about_ca_topic_score_gemma":0.001718179,"domain_scores_codex":[0.999762,0.00009182724,0.000008475458,0.00005673094,0.00005822351,0.00002271677],"domain_scores_gemma":[0.9992829,0.0004696446,0.00006407533,0.0001055045,0.00004264716,0.00003526435],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006612015,0.00004131715,0.002021592,0.0001938848,0.00005861218,0.00008308366,0.0006932439,0.08626358,0.008389192,0.849894,0.0007747355,0.05152059],"study_design_scores_gemma":[0.00001452655,0.0000426441,0.00228084,0.00004893284,0.00002143485,0.0001239618,0.0002737387,0.3784362,0.001582442,0.6127321,0.004408232,0.00003503596],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1586422,0.002137026,0.7981079,0.002988089,0.0001151655,0.00005968488,0.0001994171,0.0003294134,0.03742112],"genre_scores_gemma":[0.8180352,0.000997776,0.1785022,0.0001430726,0.00009383005,0.00007442464,0.0001341144,0.00006298875,0.001956373],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003950852,"threshold_uncertainty_score":0.01321697,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009056364265145679,"score_gpt":0.2956499705431962,"score_spread":0.2865936062780506,"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."}}