{"id":"W2050356465","doi":"10.1145/1857893.1857898","title":"A supervised combination strategy for illumination chromaticity estimation","year":2010,"lang":"en","type":"article","venue":"ACM Transactions on Applied Perception","topic":"Color Science and Applications","field":"Physics and Astronomy","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Simon Fraser University; National Natural Science Foundation of China","keywords":"Color constancy; Artificial intelligence; Computer science; Color balance; Computer vision; Standard illuminant; Chromaticity; Color normalization; Chromatic adaptation; Color model; Color histogram; Lightness; Pattern recognition (psychology); Color image; Image (mathematics); Mathematics; Color space; Image processing","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001851566,0.0001461889,0.0001183428,0.0001264563,0.0005119433,0.00009073172,0.0002117775,0.00007798523,0.001323564],"category_scores_gemma":[0.000005461544,0.0001525902,0.00009048634,0.0002770813,0.0000719509,0.0002602399,0.00000318831,0.0002041762,0.000164241],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000387313,"about_ca_system_score_gemma":0.00004360453,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004800673,"about_ca_topic_score_gemma":0.00003837185,"domain_scores_codex":[0.9990858,0.00001172047,0.000229606,0.0003025335,0.0001823146,0.0001879952],"domain_scores_gemma":[0.999271,0.00009769341,0.00008090311,0.0003856632,0.00009689071,0.00006781215],"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":[0.00002574029,0.0005290405,0.00004818059,0.00001258631,0.00001352096,1.42658e-8,0.0006178336,0.004403106,0.1993293,0.0304407,0.00008210121,0.7644979],"study_design_scores_gemma":[0.004506555,0.0005035336,0.07156648,0.00002730818,0.0002383085,0.000002627746,0.005462137,0.632442,0.05275824,0.2297258,0.001640013,0.001127002],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4607831,1.183682e-7,0.5363901,0.0003381936,0.00008463888,0.0006276068,0.00003591885,0.0000660806,0.001674304],"genre_scores_gemma":[0.9791047,6.502203e-7,0.01916337,0.00005310315,0.00008920809,0.001142087,0.0002636141,0.00001601559,0.0001672262],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7633709,"threshold_uncertainty_score":0.9995894,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02117838498338673,"score_gpt":0.2900035625477374,"score_spread":0.2688251775643506,"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."}}