{"id":"W2893960525","doi":"10.1167/18.10.219","title":"Adaptation and perceived contrast in natural vs wide-color-gamut lighting","year":2018,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Color Science and Applications","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Gamut; Standard illuminant; Contrast (vision); Chromatic adaptation; Luminance; Hue; Artificial intelligence; Adaptation (eye); Computer vision; Color constancy; Mathematics; Computer science; Color vision; Optics; Physics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001895329,0.00004261006,0.00008638035,0.00006059825,0.00008331543,0.00004279898,0.00006652104,0.00001284651,0.00003855967],"category_scores_gemma":[0.00001381005,0.000032249,0.00002758817,0.0001072085,0.00003975296,0.0002558912,0.0000162869,0.0001014337,0.00000683978],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001371269,"about_ca_system_score_gemma":0.00003066737,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002535294,"about_ca_topic_score_gemma":0.00001958054,"domain_scores_codex":[0.9995529,0.00001557385,0.0001859917,0.0000654863,0.00009834942,0.00008170946],"domain_scores_gemma":[0.9996154,0.00006285383,0.00014259,0.00004015995,0.0001019416,0.0000370484],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0002971075,0.0003541952,0.2161486,0.000008367246,0.00003364946,0.000006528362,0.009593945,0.000228615,0.3495734,0.005621735,0.001843975,0.4162899],"study_design_scores_gemma":[0.0006896789,0.000316635,0.9694749,0.00009193111,0.000009760415,0.000005031331,0.001393859,0.02253782,0.001243529,0.002081137,0.002078453,0.00007727243],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9973802,0.00003140538,0.001159601,0.0007744181,0.00008853419,0.00004924779,5.471845e-7,0.000001803893,0.000514254],"genre_scores_gemma":[0.9988012,0.000004192163,0.0008822179,0.00005016112,0.0002214557,9.444769e-7,5.509535e-7,0.000002258217,0.00003699898],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7533262,"threshold_uncertainty_score":0.1315076,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01057772674644528,"score_gpt":0.2830381384692186,"score_spread":0.2724604117227733,"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."}}