{"id":"W2081858391","doi":"10.1109/icip.2006.312652","title":"Estimating Illumination Chromaticity via Kernel Regression","year":2006,"lang":"en","type":"article","venue":"","topic":"Color Science and Applications","field":"Physics and Astronomy","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Simon Fraser University; U.S. Department of Energy","keywords":"Chromaticity; Artificial intelligence; Support vector machine; Computer science; Kernel (algebra); Pattern recognition (psychology); Kernel method; Nonparametric regression; Kernel regression; Regression; Regression analysis; Mathematics; Machine learning; Statistics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007321516,0.0006072405,0.0005535165,0.001273735,0.0002864406,0.0008293237,0.0006989081,0.0004654586,0.001666057],"category_scores_gemma":[0.003134822,0.0003036014,0.0005990298,0.001169108,0.0004021974,0.001398395,0.0007699883,0.001038008,0.001209996],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004415098,"about_ca_system_score_gemma":0.000430316,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00264545,"about_ca_topic_score_gemma":0.002442935,"domain_scores_codex":[0.9994765,0.0001103682,0.00001782921,0.0001610296,0.0001723623,0.00006180292],"domain_scores_gemma":[0.9990073,0.0002847182,0.0001822283,0.0002203626,0.0002739446,0.00003156716],"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":[0.0002909916,0.0001676617,0.01805781,0.0002152085,0.0001757254,0.0001547976,0.0002161439,0.2777359,0.1220016,0.01361693,0.004028772,0.5633383],"study_design_scores_gemma":[0.00001178742,0.00003818872,0.007484227,0.000009373189,0.00002305271,0.0001110571,0.00002412192,0.9584984,0.02763566,0.003631522,0.002484856,0.00004775822],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03504605,0.0001362327,0.9618731,0.00006004638,0.00002284596,0.00001660829,0.0001030583,0.001449018,0.001292998],"genre_scores_gemma":[0.5889415,0.0004827424,0.4060357,0.00005996037,0.00006385318,0.0000509731,0.0005774839,0.0004570016,0.003330932],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00264545,"threshold_uncertainty_score":0.005573571,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007480825980032776,"score_gpt":0.2617327893814153,"score_spread":0.2542519634013825,"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."}}