{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006136678,0.0004424289,0.0005167875,0.0004123276,0.0001967797,0.0006386841,0.0004073771,0.0006253067,0.001534347],"category_scores_gemma":[0.004927876,0.0005138842,0.0002891052,0.0001463389,0.0004062837,0.0008058252,0.0007555126,0.0006817745,0.0001597423],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004063931,"about_ca_system_score_gemma":0.0002351203,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001227724,"about_ca_topic_score_gemma":0.001429274,"domain_scores_codex":[0.9993224,0.0001549489,0.00004818706,0.000178042,0.0001580783,0.0001383486],"domain_scores_gemma":[0.9967343,0.002191006,0.0003489371,0.0001917104,0.0002498228,0.0002842298],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.000781195,0.00007887834,0.006025004,0.00006329222,0.00002585634,0.00007187381,0.00007287259,0.0001690878,0.9891222,0.0001203477,0.00005608438,0.003413382],"study_design_scores_gemma":[0.00006780662,0.001041892,0.4589313,0.00002996764,0.0001279002,0.0005794183,0.0001089069,0.01449604,0.5230664,0.0009856161,0.0005193466,0.0000453793],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9941615,0.0002145487,0.004279658,0.00005475859,0.00002077149,0.00001957452,0.00006229995,0.00005204964,0.001134807],"genre_scores_gemma":[0.997729,0.00005000168,0.001695687,0.00007416909,0.000007319528,0.0000166357,0.00006078207,0.00002845515,0.0003379251],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001534347,"threshold_uncertainty_score":0.005132914,"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."}}