{"id":"W3048928407","doi":"10.1145/3386569.3392461","title":"Nonlinear color triads for approximation, learning and direct manipulation of color distributions","year":2020,"lang":"en","type":"article","venue":"ACM Transactions on Graphics","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Representation (politics); Artificial intelligence; Nonlinear system; Parametric statistics; Color image; Extension (predicate logic); Color space; Computer vision; Image (mathematics); Mathematics; Algorithm; Image processing; Statistics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001371536,0.00009717463,0.000152833,0.00009830138,0.0003081601,0.00004231821,0.0002083031,0.00004822815,0.000004492246],"category_scores_gemma":[0.0002112184,0.00009687866,0.00008022055,0.0006084708,0.00005597463,0.000327388,0.00001043429,0.0001499603,0.000001510036],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001239071,"about_ca_system_score_gemma":0.00002659818,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002176082,"about_ca_topic_score_gemma":0.000002636646,"domain_scores_codex":[0.9992142,0.00004057447,0.0002352111,0.00025002,0.0001347446,0.0001252988],"domain_scores_gemma":[0.9991761,0.000320829,0.00010096,0.0001831905,0.0001399363,0.00007896921],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009376145,0.001582674,0.002213617,0.0007616762,0.0003929131,0.000005616709,0.00618514,0.035944,0.03720379,0.1705426,0.0005429824,0.7436874],"study_design_scores_gemma":[0.0008830543,0.0004130347,0.0003913575,0.0000220311,0.00003032172,0.000002305669,0.00008629156,0.9775289,0.008665576,0.002996938,0.008835614,0.0001445689],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004292205,0.00003740896,0.9914498,0.003623854,0.00007629069,0.0003081189,0.00003973972,0.0001273679,0.00004517761],"genre_scores_gemma":[0.7205905,0.00009199167,0.2789646,0.0002380973,0.00001786932,0.00004164879,0.0000244545,0.000009009961,0.00002182599],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9415849,"threshold_uncertainty_score":0.3950597,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03559699481953777,"score_gpt":0.296760414181061,"score_spread":0.2611634193615232,"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."}}