{"id":"W4237782444","doi":"10.32920/ryerson.14645553.v1","title":"Design and Build of an Anthropomorphic Active Vision System","year":2021,"lang":"en","type":"preprint","venue":"","topic":"CCD and CMOS Imaging Sensors","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer vision; Artificial intelligence; Computer science; Inertial measurement unit; Vergence (optics); Convolutional neural network; Human visual system model; Machine vision; Systems design; Kalman filter; Active vision; Human–computer interaction; Image (mathematics)","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":[0.0003297095,0.0004148605,0.0004633057,0.0002966922,0.0002817951,0.0007829536,0.001243986,0.0009036418,0.005549538],"category_scores_gemma":[0.0004577289,0.0003705129,0.0004022258,0.000131119,0.0003623311,0.000739116,0.0008400973,0.0005743771,0.001995492],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002319915,"about_ca_system_score_gemma":0.000441655,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005513387,"about_ca_topic_score_gemma":0.0004307453,"domain_scores_codex":[0.9997539,0.00002733571,0.0000162323,0.00008365069,0.00009826295,0.00002066074],"domain_scores_gemma":[0.9998422,0.00002687232,0.00001902711,0.00003068278,0.00005834073,0.00002292172],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002702038,0.0002239168,0.001719848,0.0008356754,0.0001409551,0.0008111455,0.0007346128,0.1275953,0.3909379,0.04227979,0.005463155,0.4289876],"study_design_scores_gemma":[0.0001268481,0.001253457,0.002635158,0.0001357663,0.0001207577,0.001377347,0.0001577402,0.7646751,0.1107221,0.01000815,0.1087055,0.00008220744],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0138715,0.000129406,0.9790684,0.0001317962,0.00008350171,0.0001676714,0.0000647915,0.001506154,0.00497685],"genre_scores_gemma":[0.3155443,0.0002673531,0.668937,0.0001618634,0.00004026608,0.0004760519,0.0002160477,0.00014639,0.01421075],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005549538,"threshold_uncertainty_score":0.01856512,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01200169554733112,"score_gpt":0.241171244543613,"score_spread":0.2291695489962819,"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."}}