{"id":"W2151626462","doi":"10.1016/j.visres.2011.05.012","title":"Disparity biasing in depth from monocular occlusions","year":2011,"lang":"en","type":"article","venue":"Vision Research","topic":"Visual perception and processing mechanisms","field":"Neuroscience","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Monocular; Depth perception; Stereopsis; Binocular disparity; Computer vision; Perception; Occlusion; Artificial intelligence; Computer science; Psychology; Neuroscience; Medicine","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":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00114256,0.000092721,0.0001177588,0.0003754506,0.0004219754,0.0001175215,0.0004087341,0.0001038012,0.002648108],"category_scores_gemma":[0.001451007,0.00007918984,0.00003818688,0.0009672548,0.0001419384,0.0002296134,0.0003616972,0.0005469149,0.001308991],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005204011,"about_ca_system_score_gemma":0.00006339438,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001045749,"about_ca_topic_score_gemma":0.0001899935,"domain_scores_codex":[0.9974848,0.0006684058,0.0002047829,0.0004928705,0.0007220528,0.0004271103],"domain_scores_gemma":[0.9991034,0.0002845159,0.00002764497,0.0003294226,0.00008531522,0.0001697003],"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":[0.0001071331,0.0004304088,0.002873242,0.00001138548,8.095952e-7,0.00009604543,0.00276297,0.000003067435,0.9325143,0.001206554,0.001031417,0.0589627],"study_design_scores_gemma":[0.001089109,0.000457244,0.1232558,0.0002881427,0.00000328527,0.000008248368,0.00091542,0.03301707,0.7937958,0.04122121,0.005527097,0.0004216076],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9784737,0.00003165881,0.001638259,0.000332974,0.0001443673,0.0001967512,0.000008539519,0.00007608599,0.0190977],"genre_scores_gemma":[0.9968671,0.00006583984,0.0020448,0.000375643,0.00003432828,0.00001150691,0.000003094283,0.00001635499,0.0005813016],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1387185,"threshold_uncertainty_score":0.9994686,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4421114385261212,"score_gpt":0.4825485602443257,"score_spread":0.04043712171820452,"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."}}