{"id":"W2122455999","doi":"10.1097/wnr.0b013e32833f47a3","title":"A novel way to make transient-VEPs a better predictor of human binocular integration","year":2010,"lang":"en","type":"article","venue":"Neuroreport","topic":"Visual perception and processing mechanisms","field":"Neuroscience","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal; Centre Hospitalier Universitaire Sainte-Justine; Université de Montréal","funders":"","keywords":"Monocular; Binocular vision; Binocular disparity; Stereoscopy; Psychology; Electrophysiology; Optics; Artificial intelligence; Computer science; Neuroscience; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005753025,0.0005100517,0.0004421055,0.0007707928,0.0001063483,0.0004746832,0.0002982409,0.000389406,0.001155732],"category_scores_gemma":[0.002076388,0.0001234814,0.0001515058,0.0004712824,0.000256201,0.0007497407,0.000343709,0.0004532447,0.0002607196],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001371299,"about_ca_system_score_gemma":0.0001751938,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002872709,"about_ca_topic_score_gemma":0.0006269323,"domain_scores_codex":[0.9998057,0.0000560662,0.00002347487,0.0000556801,0.0000447844,0.00001418981],"domain_scores_gemma":[0.9992005,0.0002700881,0.000174115,0.0001014728,0.0001701968,0.0000835411],"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":[0.0007578681,0.0002755176,0.05478122,0.0003776708,0.0001317918,0.0003590261,0.0001360142,0.00182543,0.8135102,0.001820045,0.000874782,0.1251505],"study_design_scores_gemma":[0.0001851478,0.00394008,0.5688423,0.0000824845,0.0002535696,0.007443331,0.000230865,0.09170297,0.3156294,0.006160134,0.005342891,0.0001868473],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.6033292,0.001034339,0.3898447,0.0001370621,0.0001668399,0.0001990047,0.001151852,0.001041,0.003095819],"genre_scores_gemma":[0.8802491,0.0003537815,0.117992,0.00007998585,0.0001102757,0.000187489,0.0005241957,0.0000408136,0.0004624674],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.001155732,"threshold_uncertainty_score":0.003866315,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05831035724498249,"score_gpt":0.3237300237772241,"score_spread":0.2654196665322416,"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."}}