{"id":"W2001959966","doi":"10.1115/msec_icmp2008-72374","title":"Force/Velocity Control of a Pneumatic Gantry Robot for Contour Tracking With Neural Network Compensation","year":2008,"lang":"en","type":"article","venue":"","topic":"Robot Manipulation and Learning","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Control theory (sociology); Compensation (psychology); Controller (irrigation); Tracking (education); Lag; Robot; Artificial neural network; PID controller; Computer science; Engineering; Control engineering; Artificial intelligence; Control (management)","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0000977759,0.0001083258,0.0002303407,0.00003384342,0.00009773112,0.00001312457,0.00005348379,0.00004369616,0.0000499989],"category_scores_gemma":[0.00002053825,0.00009298341,0.00005027219,0.00007400458,0.00002199197,0.000130095,0.00000318447,0.00008716262,0.000002554983],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002053731,"about_ca_system_score_gemma":0.000009560092,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001313923,"about_ca_topic_score_gemma":0.00004046044,"domain_scores_codex":[0.9993494,0.00002051775,0.0002461411,0.00008912686,0.0001057934,0.0001889922],"domain_scores_gemma":[0.9995871,0.0001490791,0.00006801653,0.00009056769,0.00006620839,0.00003903759],"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":[0.00003194028,0.000006080384,0.006523765,0.00005594848,0.00003324759,0.000001117282,0.0002650463,0.9909248,0.0008127214,0.0005016407,0.0001403048,0.0007034055],"study_design_scores_gemma":[0.001164929,0.00005307814,0.05479017,0.0000314344,0.00001713583,0.00001651598,0.00005672873,0.9435328,0.0001484691,0.00003101346,0.00004382978,0.0001138781],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1528456,0.00005296584,0.8454105,0.00004272579,0.00008192063,0.000326935,3.844874e-7,0.0001775176,0.001061441],"genre_scores_gemma":[0.9927842,0.000003164122,0.006853688,0.00006988796,0.00008517605,0.00001632124,0.000009294807,0.00002499426,0.0001532446],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8399386,"threshold_uncertainty_score":0.3791753,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02920130942270047,"score_gpt":0.2214782963058774,"score_spread":0.1922769868831769,"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."}}