{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003618991,0.0001793581,0.0001509622,0.0002878963,0.0001185968,0.0005035011,0.0003028123,0.0002239013,0.002023327],"category_scores_gemma":[0.003911968,0.0001735653,0.0001657771,0.0001270324,0.000354101,0.0006949165,0.0009743246,0.0002367736,0.0001059998],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002380307,"about_ca_system_score_gemma":0.0001144992,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006981671,"about_ca_topic_score_gemma":0.0003796322,"domain_scores_codex":[0.9995462,0.00007734847,0.00002685059,0.0000835348,0.000225608,0.00004048735],"domain_scores_gemma":[0.9985324,0.0006408427,0.0003160481,0.0002313598,0.0001904881,0.00008892943],"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":[0.0005399598,0.00001984708,0.004908151,0.0001351265,0.00001199793,0.00008244576,0.0002813978,0.001482047,0.9705541,0.0008357786,0.00008502621,0.02106403],"study_design_scores_gemma":[0.00007827358,0.001693696,0.5492297,0.00006949166,0.00005934478,0.00194339,0.0006534762,0.02983524,0.4109189,0.002329096,0.003098635,0.00009068281],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.99058,0.000198255,0.007359192,0.00002154503,0.000006820876,0.000006818672,0.00004940457,0.00004743783,0.001730467],"genre_scores_gemma":[0.9974763,0.00004001948,0.0021885,0.000011974,0.000002336974,0.000005704002,0.00005702346,0.00001006375,0.0002081128],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002023327,"threshold_uncertainty_score":0.006768763,"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."}}