{"id":"W2518911388","doi":"10.1167/16.11.11","title":"Depth discrimination from occlusions in 3D clutter","year":2016,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Visual perception and processing mechanisms","field":"Neuroscience","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Clutter; Depth perception; Computer vision; Artificial intelligence; Parallax; Computer science; Binocular disparity; Observer (physics); Occlusion; Sensory cue; Stereopsis; Perception; Radar; Psychology; Physics","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":[0.0008139585,0.0003450875,0.000498973,0.0004731457,0.00031349,0.0008281837,0.0002843097,0.0005223926,0.001333378],"category_scores_gemma":[0.006585238,0.0003709875,0.0002827923,0.0002027357,0.0008573899,0.001357728,0.001816973,0.0005611722,0.0001467235],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000456765,"about_ca_system_score_gemma":0.0002619307,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001109642,"about_ca_topic_score_gemma":0.0009427081,"domain_scores_codex":[0.9992759,0.000152228,0.00003899476,0.000171092,0.0002271979,0.0001347043],"domain_scores_gemma":[0.9969458,0.001779497,0.000511645,0.0003211851,0.0002158147,0.0002261005],"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":[0.001810055,0.00009371902,0.007949595,0.0001626624,0.00003920009,0.0001173143,0.0005989263,0.004444421,0.9562137,0.001681825,0.0002533495,0.02663535],"study_design_scores_gemma":[0.0002899501,0.003690064,0.365898,0.0001496351,0.0002331428,0.001917031,0.001165033,0.1289332,0.4705743,0.02349485,0.003369796,0.0002850375],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9709825,0.0002479732,0.02669916,0.0000400829,0.00001942439,0.00002480863,0.00007232468,0.00007801801,0.001835702],"genre_scores_gemma":[0.992564,0.0001497129,0.006820994,0.00005812943,0.00001139186,0.00001307959,0.000107491,0.00003759057,0.0002375729],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001333378,"threshold_uncertainty_score":0.004460633,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05602847291832257,"score_gpt":0.3606397514172042,"score_spread":0.3046112784988816,"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."}}