{"id":"W2618018761","doi":"10.11159/eee17.113","title":"Camera Color Correction Using Normalized Signal Polynomial Regression","year":2017,"lang":"en","type":"article","venue":"Proceedings of the World Congress on Electrical Engineering and Computer Systems and Science","topic":"Color Science and Applications","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Polynomial regression; Polynomial; Regression; Artificial intelligence; SIGNAL (programming language); Computer vision; Color correction; Regression analysis; Pattern recognition (psychology); Statistics; Mathematics; Machine learning; Image (mathematics); Mathematical analysis","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002052712,0.00009667587,0.0001453445,0.00009464072,0.0007789179,0.000409647,0.0003742412,0.00001487647,8.29189e-7],"category_scores_gemma":[0.000008449159,0.00006429734,0.00002873133,0.00026745,0.0001902503,0.0002421174,0.0001479163,0.0001173232,2.098593e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001859138,"about_ca_system_score_gemma":0.0000311526,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001252666,"about_ca_topic_score_gemma":5.379334e-7,"domain_scores_codex":[0.9992521,0.000002244512,0.0001354178,0.0002293226,0.0001889623,0.0001919594],"domain_scores_gemma":[0.9995329,0.00002588218,0.0001699246,0.0000997412,0.00009268045,0.00007889955],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001421203,0.000329516,0.1727363,0.0001664772,0.00009586252,0.000001181057,0.00057173,0.02204558,0.4331348,0.2215217,0.00412256,0.1451322],"study_design_scores_gemma":[0.0001686591,0.00004519667,0.00804395,0.0001419058,0.000007909936,0.000004832775,0.00001312909,0.9820484,0.008904342,0.00001559095,0.0005106921,0.00009537863],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9974534,0.00002791697,0.000830338,0.0000998465,0.0009641185,0.0001588488,0.000001168424,0.00001500776,0.0004493505],"genre_scores_gemma":[0.9992443,0.000001383549,0.0001901889,0.000007811037,0.000213686,0.00001113495,5.519442e-8,0.000003579261,0.0003278966],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9600028,"threshold_uncertainty_score":0.5990885,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00935004567510316,"score_gpt":0.2399515445044578,"score_spread":0.2306014988293546,"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."}}