{"id":"W2894519885","doi":"10.1167/18.10.508","title":"Depth constancy for virtual and physical objects","year":2018,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Computer Graphics and Visualization Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Depth perception; Binocular disparity; Stereoscopy; Stereopsis; Monocular; Computer vision; Artificial intelligence; Ranging; Perception; Computer science; Virtual reality; Range (aeronautics); Virtual image; Geometry; Mathematics; Psychology; Engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002290942,0.00005326,0.0001117392,0.0001159469,0.0000718451,0.0001009367,0.0002121765,0.00002308286,9.831263e-7],"category_scores_gemma":[0.00002875309,0.00004041547,0.0000535843,0.0001397211,0.00004447688,0.0003067571,0.00008302243,0.00005009233,7.370438e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007034974,"about_ca_system_score_gemma":0.00003437078,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":3.67375e-7,"about_ca_topic_score_gemma":8.428561e-7,"domain_scores_codex":[0.9995008,0.00001969373,0.0001619872,0.0000924415,0.0001484373,0.00007662852],"domain_scores_gemma":[0.9993241,0.00007569198,0.0001507346,0.0001000845,0.0002910505,0.00005834819],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004584071,0.0002165824,0.0003493662,0.00001571868,0.00002363635,0.000007835394,0.002303103,6.178576e-7,0.007451952,0.7237625,0.01094807,0.2548748],"study_design_scores_gemma":[0.003131552,0.02327918,0.02431462,0.0005831239,0.00003642361,0.0003569824,0.000112361,0.5748304,0.05906408,0.2775705,0.03616102,0.0005598299],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.104165,0.0000542678,0.8952093,0.0001194138,0.0002461108,0.0000489941,3.537674e-7,0.00002181845,0.0001347058],"genre_scores_gemma":[0.9665853,0.00003018602,0.03295664,0.0001519027,0.0002615835,4.696325e-7,1.086118e-7,0.000003368166,0.00001044138],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8624203,"threshold_uncertainty_score":0.1648095,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01699606139023336,"score_gpt":0.3367279631033636,"score_spread":0.3197319017131302,"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."}}