{"id":"W3196785225","doi":"10.1167/jov.21.9.1896","title":"Scaling stereoscopic depth through reaching","year":2021,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Spatial Cognition and Navigation","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Depth perception; Computer science; Stereopsis; Artificial intelligence; Proprioception; Task (project management); Computer vision; Scaling; Perception; Stereoscopy; Visibility; Mathematics; Psychology; Geometry; Optics; 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":[0.0004263265,0.000374615,0.0002651648,0.0003412874,0.0001152564,0.0004660999,0.0003580294,0.0002929343,0.002220338],"category_scores_gemma":[0.00465106,0.000270434,0.000260376,0.0001509406,0.0003828083,0.0009044968,0.001183832,0.0003988076,0.0001938563],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003758221,"about_ca_system_score_gemma":0.0003543872,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001882579,"about_ca_topic_score_gemma":0.00131803,"domain_scores_codex":[0.9993479,0.0001323407,0.00004949428,0.000118714,0.0002727965,0.00007878591],"domain_scores_gemma":[0.998096,0.0007669551,0.0004308015,0.0003369091,0.0002478877,0.0001213651],"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":[0.0004432092,0.00008622135,0.00319751,0.000111099,0.00001481395,0.00006439127,0.0004379658,0.002552644,0.9512994,0.0006099924,0.0000983832,0.04108449],"study_design_scores_gemma":[0.000154628,0.005927346,0.2999901,0.0001127458,0.0000949009,0.00111583,0.0005726221,0.07348047,0.6100501,0.004041482,0.004330683,0.0001290653],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9816949,0.00008667292,0.01636219,0.00002689002,0.000006911061,0.00003868826,0.00004319046,0.0001441822,0.001596449],"genre_scores_gemma":[0.9914574,0.0000644047,0.007930923,0.0000150625,0.000002599163,0.00001743369,0.00003849024,0.00002247153,0.000451266],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002220338,"threshold_uncertainty_score":0.007427752,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02210606570381113,"score_gpt":0.2964549448681957,"score_spread":0.2743488791643846,"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."}}