{"id":"W4235075165","doi":"10.32920/ryerson.14647602","title":"A Novel Position Domain Controller For Contour Tracking Performance Improvement","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Iterative Learning Control Systems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Control theory (sociology); Position (finance); Controller (irrigation); Computer science; Feed forward; Contouring; Frequency domain; Time domain; Numerical control; Tracking error; Motion control; Domain (mathematical analysis); Control engineering; Machining; Artificial intelligence; Engineering; Computer vision; Mathematics; Control (management); Robot","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":[0.000289834,0.0004378381,0.0003455885,0.0003111197,0.0003615883,0.0006610501,0.0008652536,0.0006044276,0.002147353],"category_scores_gemma":[0.0006100254,0.0001410211,0.0002443926,0.0002057216,0.0003279862,0.0004637672,0.000421714,0.0006536691,0.0004752713],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000358974,"about_ca_system_score_gemma":0.0005365023,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001520235,"about_ca_topic_score_gemma":0.0012715,"domain_scores_codex":[0.9997324,0.00002313644,0.0000181726,0.00006792686,0.0001402663,0.00001813681],"domain_scores_gemma":[0.9997173,0.00004957943,0.00003217263,0.00002839444,0.0001609477,0.00001148665],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003597013,0.0002872979,0.000731078,0.0004851533,0.00006437801,0.000447376,0.0003378479,0.2420753,0.2254041,0.02566054,0.005161853,0.4989853],"study_design_scores_gemma":[0.00006813516,0.0003703143,0.0004642608,0.00002108687,0.00002147891,0.0001790385,0.000013551,0.9613962,0.02814972,0.001052271,0.00824017,0.00002370155],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0198649,0.0003341729,0.9691417,0.0001231294,0.0002094158,0.0001029531,0.00002530192,0.001051282,0.009147208],"genre_scores_gemma":[0.8494594,0.0004298026,0.1367961,0.0002136891,0.0001238499,0.0002397851,0.00008785848,0.00005571341,0.01259375],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002147353,"threshold_uncertainty_score":0.007183611,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01191982005724734,"score_gpt":0.2254429538959761,"score_spread":0.2135231338387288,"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."}}