{"id":"W2126487483","doi":"10.1167/9.1.10","title":"Binocular depth discrimination and estimation beyond interaction space","year":2009,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Visual perception and processing mechanisms","field":"Neuroscience","cited_by":101,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Binocular disparity; Monocular; Depth perception; Stereopsis; Artificial intelligence; Binocular vision; Computer vision; Computer science; Stereoscopy; Mathematics; Psychology; Perception","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":[0.0003777792,0.0002521439,0.0003404751,0.0004588961,0.0001430551,0.0005689549,0.0004199905,0.0003615983,0.001471766],"category_scores_gemma":[0.002681483,0.0002097363,0.0002001348,0.0002605658,0.0004609999,0.001268,0.001168109,0.0005127865,0.0001671375],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003513101,"about_ca_system_score_gemma":0.00024335,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001042094,"about_ca_topic_score_gemma":0.0007578073,"domain_scores_codex":[0.9996138,0.00005892116,0.00001922992,0.00007499022,0.0001586617,0.00007446826],"domain_scores_gemma":[0.9989473,0.0004309856,0.0002920397,0.0001284102,0.0001093456,0.00009198433],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.001036398,0.00003937614,0.008240828,0.0001621573,0.00003050891,0.0001297182,0.0002602754,0.001372492,0.914099,0.002517143,0.0001764013,0.07193565],"study_design_scores_gemma":[0.0001159866,0.002034249,0.4681586,0.00008758133,0.0000843806,0.00183712,0.0003898892,0.02868545,0.4812514,0.01354917,0.003683144,0.0001230379],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.977122,0.001324327,0.01813613,0.0001016658,0.0000152827,0.00001296754,0.0001137729,0.0000822477,0.003091508],"genre_scores_gemma":[0.9966778,0.0001695556,0.002870221,0.00002153342,0.000006573205,0.000003504235,0.00005316658,0.000008102958,0.0001896466],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001471766,"threshold_uncertainty_score":0.004923522,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04065600374643451,"score_gpt":0.3732164533141479,"score_spread":0.3325604495677134,"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."}}