{"id":"W2292820037","doi":"10.1167/16.3.23","title":"Detection of between-eye differences in color: Interactions with luminance","year":2016,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Visual perception and processing mechanisms","field":"Neuroscience","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"Montreal General Hospital","funders":"Canadian Institutes of Health Research","keywords":"Luminance; Chromatic scale; Contrast (vision); Hue; Optics; Ocular dominance; Computer vision; Artificial intelligence; Psychology; Communication; Mathematics; Computer science; Physics; Neuroscience; Visual cortex","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001818109,0.00005633281,0.0001461823,0.0001706912,0.00004372714,0.00001522651,0.0001004759,0.0000296571,0.00004795643],"category_scores_gemma":[0.0001696671,0.00002986025,0.00003198121,0.0001968977,0.00005395178,0.0003755842,0.00001272723,0.0001109206,0.000007664194],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003607185,"about_ca_system_score_gemma":0.00002647104,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000239712,"about_ca_topic_score_gemma":0.00001246778,"domain_scores_codex":[0.9992594,0.00007722904,0.000277384,0.00008823985,0.000220547,0.00007725039],"domain_scores_gemma":[0.9993422,0.0001395034,0.0003568568,0.00005395497,0.0000704069,0.00003708303],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001281358,0.00005626637,0.003762797,0.000006742686,8.73655e-7,0.000003096263,0.0001254048,0.000001250523,0.9494454,0.00001877929,0.000007015304,0.04644427],"study_design_scores_gemma":[0.000668067,0.001677857,0.2183479,0.0006916902,0.000007288569,0.00003505946,0.00009166894,0.0001056878,0.7775746,0.0004164766,0.0003193408,0.00006438273],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9866463,0.000007836657,0.01273123,0.000306411,0.0001841473,0.00003242051,0.000001427565,0.000005101862,0.00008514784],"genre_scores_gemma":[0.9994609,0.00005633285,0.0001991121,0.00002961841,0.00004951566,6.37204e-7,2.419456e-8,0.000004465109,0.0001994406],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2145851,"threshold_uncertainty_score":0.1217666,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05203919853587868,"score_gpt":0.3514373359949211,"score_spread":0.2993981374590424,"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."}}