{"id":"W2084103710","doi":"10.1167/14.10.734","title":"Dynamic perspective cues enhance depth from motion parallax","year":2014,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Visual perception and processing mechanisms","field":"Neuroscience","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"","keywords":"Parallax; Perspective (graphical); Motion (physics); Depth perception; Computer science; Kinetic depth effect; Computer vision; Communication; Artificial intelligence; Psychology; Motion perception; Neuroscience; Perception","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":[0.0001019955,0.0004957812,0.0003047086,0.0002647692,0.0001455454,0.0006434871,0.0002807477,0.0004853453,0.007917286],"category_scores_gemma":[0.001267884,0.0002881613,0.0002651833,0.0002173129,0.0001736216,0.001194859,0.0008338256,0.0007512376,0.0004996712],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002308371,"about_ca_system_score_gemma":0.0002644392,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000517564,"about_ca_topic_score_gemma":0.0007074161,"domain_scores_codex":[0.999911,0.00000528875,0.00000351878,0.00001711628,0.00003338913,0.00002963874],"domain_scores_gemma":[0.9996858,0.0001144003,0.00006365323,0.00002384851,0.00004032217,0.00007203226],"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":[0.0007713431,0.00004938665,0.0003825506,0.0001036541,0.00001326435,0.00009242299,0.00003657644,0.0005225025,0.9685627,0.001057565,0.0004073032,0.02800065],"study_design_scores_gemma":[0.0006915184,0.002635801,0.1975265,0.0001838532,0.0003064123,0.002033094,0.0004534198,0.0424251,0.7283264,0.01397088,0.01134857,0.0000984427],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9484164,0.002515671,0.03466867,0.000416641,0.0002471994,0.00005585371,0.0003732423,0.0002109912,0.01309535],"genre_scores_gemma":[0.9889414,0.001252674,0.005607537,0.0001524061,0.00006736941,0.00001682676,0.000171918,0.0001238166,0.003666203],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007917286,"threshold_uncertainty_score":0.02648598,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02677964868619843,"score_gpt":0.3628663915267977,"score_spread":0.3360867428405993,"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."}}