{"id":"W1530411632","doi":"10.2352/cic.2003.11.1.art00028","title":"A Large Image Database for Color Constancy Research","year":2003,"lang":"en","type":"article","venue":"Color and Imaging Conference","topic":"Color Science and Applications","field":"Physics and Astronomy","cited_by":216,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Standard illuminant; Color constancy; Artificial intelligence; RGB color model; Color balance; Computer vision; Chromaticity; Computer science; ICC profile; Color space; Color histogram; Color image; Computer graphics (images); Gray (unit); Digital camera; Color model; 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":[0.002280407,0.001207568,0.001651248,0.007201537,0.0009771967,0.001882782,0.002909463,0.0017901,0.008719879],"category_scores_gemma":[0.0103156,0.0005795264,0.000943005,0.009147632,0.000377527,0.00310027,0.001358073,0.001129952,0.007008586],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001139682,"about_ca_system_score_gemma":0.001251516,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006864607,"about_ca_topic_score_gemma":0.008729732,"domain_scores_codex":[0.9974656,0.0002995393,0.0003323791,0.0006346588,0.001105326,0.0001624806],"domain_scores_gemma":[0.9919481,0.001630483,0.0004278716,0.003335682,0.002318885,0.000339072],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001857263,0.001675146,0.01199786,0.002368835,0.0003775219,0.0009492456,0.0001903262,0.01097547,0.04157327,0.007211032,0.3044726,0.6163514],"study_design_scores_gemma":[0.0008350105,0.001203841,0.117237,0.0005260254,0.000528315,0.005926993,0.0009830532,0.2771446,0.1280692,0.01594961,0.4511914,0.000405038],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.1902131,0.01176519,0.2414852,0.00180527,0.0006869151,0.003069414,0.4883461,0.04018738,0.02244141],"genre_scores_gemma":[0.1432866,0.00231935,0.249109,0.000372908,0.0001563456,0.001563958,0.5981142,0.001029001,0.004048621],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008719879,"threshold_uncertainty_score":0.02917093,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05597834306926116,"score_gpt":0.3835748897408096,"score_spread":0.3275965466715484,"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."}}