{"id":"W2078445486","doi":"10.1109/acc.2013.6580826","title":"Catching moving objects using a Navigation Guidance technique in a robotic Visual Servoing system","year":2013,"lang":"en","type":"article","venue":"","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Visual servoing; Computer vision; Artificial intelligence; Computer science; Object (grammar); Navigation system; Controller (irrigation); Guidance system; Robot; 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":[0.0003283649,0.000142276,0.0001718127,0.0001814966,0.0001425323,0.0002607673,0.0003805358,0.00004345028,0.000004503585],"category_scores_gemma":[0.00003745222,0.0001343199,0.00003410926,0.0006157793,0.00001310518,0.00211495,0.000270256,0.0001780824,0.00003062889],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000269416,"about_ca_system_score_gemma":0.00005867679,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007846738,"about_ca_topic_score_gemma":0.0000114357,"domain_scores_codex":[0.9986642,0.00008147028,0.0003343781,0.0003789994,0.0002091668,0.0003317853],"domain_scores_gemma":[0.9993847,0.00006086399,0.0001151463,0.0002875206,0.00008120217,0.00007055349],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000003229675,0.00009987599,0.01005936,0.0005317008,0.00001036364,0.0001098552,0.00395593,0.1148223,0.7757106,0.01952707,0.00002797271,0.0751417],"study_design_scores_gemma":[0.0001197008,0.00001134274,0.000935033,0.0009501528,9.375232e-7,0.0000814747,0.0004522421,0.9838957,0.01274214,0.0006350833,0.000002665953,0.0001735012],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07305534,0.00005271286,0.9251422,0.00008375914,0.0001283218,0.000375813,3.460007e-8,0.0003512815,0.0008105747],"genre_scores_gemma":[0.5868174,3.763951e-7,0.4130116,0.00009781855,0.0000160118,0.00002549124,2.535261e-7,0.000007927216,0.00002305536],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8690735,"threshold_uncertainty_score":0.5477406,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01216157104977029,"score_gpt":0.2860604333434134,"score_spread":0.2738988622936431,"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."}}