{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004128549,0.0001491024,0.0002134852,0.0001114218,0.00006532843,0.00007297011,0.0002617127,0.0001322609,0.0003563703],"category_scores_gemma":[0.0005615343,0.0001515282,0.00006587536,0.0005287531,0.00001256808,0.00008245622,0.00005098685,0.001141695,0.0005104573],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006524625,"about_ca_system_score_gemma":0.00001305259,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002647036,"about_ca_topic_score_gemma":4.821857e-7,"domain_scores_codex":[0.9985308,0.00005034107,0.0002028312,0.0002257973,0.000466237,0.0005240181],"domain_scores_gemma":[0.9991783,0.0002244347,0.000007507243,0.0001816746,0.00006468888,0.0003434182],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000006843971,0.000008549554,0.00001551023,0.00007884297,0.00002311271,0.00002139645,0.00007806204,0.9700563,0.02180045,0.006274029,0.0009572551,0.0006796613],"study_design_scores_gemma":[0.0003650234,0.00007711885,0.0003070594,0.00002231628,0.00000473814,0.000003707602,0.00001977061,0.990913,0.001174827,0.00005934194,0.006891947,0.0001611475],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007946117,0.0004814696,0.9838328,0.003008899,0.0004228897,0.0004035472,8.827742e-7,0.001840294,0.002063083],"genre_scores_gemma":[0.9981862,0.00001838014,0.001136302,0.0001255902,0.0003690741,0.00002490932,0.00000372671,0.00006931642,0.00006645394],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9902402,"threshold_uncertainty_score":0.6561065,"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."}}