{"id":"W1970141893","doi":"10.1364/josaa.19.002374","title":"Estimating the scene illumination chromaticity by using a neural network","year":2002,"lang":"en","type":"article","venue":"Journal of the Optical Society of America A","topic":"Color Science and Applications","field":"Physics and Astronomy","cited_by":188,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; Simon Fraser University","keywords":"Chromaticity; Artificial intelligence; Color constancy; Computer vision; Computer science; Artificial neural network; Object (grammar); Set (abstract data type); Photography; Color balance; Pattern recognition (psychology); Color image; Image processing; Image (mathematics)","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003263533,0.000512647,0.0003036089,0.0005911695,0.0002992467,0.000596809,0.0004878587,0.0005370468,0.001108781],"category_scores_gemma":[0.001114452,0.0003270594,0.0003781862,0.0005253399,0.0002912281,0.0008520469,0.0003904663,0.0006522111,0.0003309106],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007080316,"about_ca_system_score_gemma":0.0004020615,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008308436,"about_ca_topic_score_gemma":0.008172242,"domain_scores_codex":[0.9998639,0.00001906902,0.000004608105,0.00005887399,0.00003329691,0.00002030126],"domain_scores_gemma":[0.9997843,0.00006123798,0.00002642689,0.00002966733,0.00008527077,0.00001310692],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003042409,0.0001081332,0.005108634,0.00009409165,0.0001130578,0.0001207709,0.00007243591,0.5183644,0.09864918,0.003450254,0.001820112,0.3717947],"study_design_scores_gemma":[0.000004542767,0.00001383432,0.001009235,0.000004577131,0.00001101273,0.00002043769,0.000004911598,0.989625,0.008147182,0.0007887934,0.0003617988,0.000008637076],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09412624,0.0003200783,0.9013214,0.0001604476,0.00005743583,0.0000315412,0.0001196651,0.001377773,0.002485319],"genre_scores_gemma":[0.7323448,0.0003871333,0.2634555,0.00008850045,0.00005299618,0.00004695569,0.0002323007,0.00007633946,0.003315355],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008308436,"threshold_uncertainty_score":0.01652014,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01719245963320916,"score_gpt":0.2637193647661908,"score_spread":0.2465269051329816,"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."}}