{"id":"W3005782798","doi":"10.5539/mer.v9n2p51","title":"Gesture Control Robotic Arm","year":2020,"lang":"en","type":"article","venue":"Mechanical Engineering Research","topic":"Robot Manipulation and Learning","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Joystick; Robotic arm; Gesture; Computer science; Robot end effector; Controller (irrigation); Set (abstract data type); Artificial intelligence; Motion (physics); Scope (computer science); Keypad; Position (finance); Control engineering; Human–computer interaction; Robot; Computer vision; Simulation; Engineering; Computer hardware","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003010153,0.001015371,0.0006332908,0.0008803316,0.0007096264,0.001054945,0.001569857,0.001264677,0.03391504],"category_scores_gemma":[0.001053876,0.0002634947,0.0004688759,0.0005204692,0.000656969,0.001030138,0.0009130199,0.0006993336,0.01530255],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003900634,"about_ca_system_score_gemma":0.0004879469,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002608959,"about_ca_topic_score_gemma":0.001873247,"domain_scores_codex":[0.9994413,0.00003575061,0.00003492268,0.0001442647,0.0002869933,0.00005668635],"domain_scores_gemma":[0.9996805,0.00004798335,0.00005221215,0.0000757413,0.0001100069,0.00003357777],"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":[0.0005980853,0.0001094559,0.000876021,0.001409659,0.00004866971,0.0009067159,0.0002440844,0.008302721,0.1844718,0.01427263,0.04222177,0.7465385],"study_design_scores_gemma":[0.0004390914,0.001798625,0.01196437,0.0008402826,0.0002742651,0.007352594,0.0002897543,0.1248588,0.1921374,0.02062888,0.6389776,0.0004383387],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02238533,0.007579565,0.715232,0.001100268,0.00145866,0.0008197112,0.00127095,0.02664945,0.2235041],"genre_scores_gemma":[0.5530781,0.004991171,0.1865397,0.001502808,0.000457153,0.001080181,0.002039908,0.0006804724,0.2496306],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03391504,"threshold_uncertainty_score":0.1134571,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0667066762233483,"score_gpt":0.2910590105417968,"score_spread":0.2243523343184485,"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."}}