{"id":"W2515775949","doi":"10.1167/16.12.198","title":"Depth discrimination from occlusions in 3D clutter scenes","year":2016,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Infrared Target Detection Methodologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Clutter; Parallax; Depth perception; Artificial intelligence; Computer vision; Computer science; Visibility; Context (archaeology); Sensory cue; Geology; Perception; Psychology; Radar; Optics; 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":[0.0003402244,0.0003776208,0.0004192395,0.0005275051,0.0002614083,0.0007522388,0.0002939613,0.0004558563,0.001478084],"category_scores_gemma":[0.003183459,0.0003083419,0.000323471,0.0002081012,0.0004503096,0.001007147,0.001342089,0.0004811981,0.0001856076],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003886974,"about_ca_system_score_gemma":0.0002320063,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001921954,"about_ca_topic_score_gemma":0.001567365,"domain_scores_codex":[0.9995629,0.00005590582,0.00002005863,0.00008468822,0.0001623873,0.0001140595],"domain_scores_gemma":[0.9987788,0.0005937466,0.0002808178,0.0001094037,0.00008860113,0.0001486451],"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":[0.001399422,0.00009519371,0.009527664,0.0001936548,0.0000311947,0.0001814327,0.0006027928,0.002557063,0.9537729,0.0007056956,0.0002394722,0.03069366],"study_design_scores_gemma":[0.0001793808,0.00210176,0.6109887,0.0001000833,0.0001485948,0.00142532,0.0007921109,0.05844335,0.3183745,0.004756765,0.002534003,0.0001555657],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9885407,0.0001450716,0.009221362,0.00002304236,0.000008526104,0.00001939307,0.00005931964,0.00008037091,0.001902362],"genre_scores_gemma":[0.9956608,0.0001204154,0.003686268,0.00005693879,0.00000765074,0.00001192931,0.0001538666,0.00002513989,0.000276919],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001921954,"threshold_uncertainty_score":0.004944682,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02870010320280664,"score_gpt":0.2988186326665465,"score_spread":0.2701185294637398,"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."}}