{"id":"W4244180429","doi":"10.32920/ryerson.14647602.v1","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); Controller (irrigation); Position (finance); Computer science; Feed forward; Contouring; Frequency domain; Time domain; Numerical control; Domain (mathematical analysis); Tracking error; Motion control; Control engineering; Artificial intelligence; Machining; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004210847,0.000400001,0.0006473009,0.0001059863,0.00009407649,0.0003769763,0.0001843925,0.0003078007,0.00004443149],"category_scores_gemma":[0.00002898145,0.0003931434,0.0002642815,0.00005071776,0.00001412983,0.0001474631,0.00008374804,0.0005123172,0.000008340039],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003623833,"about_ca_system_score_gemma":0.00004965688,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003744997,"about_ca_topic_score_gemma":0.00003462505,"domain_scores_codex":[0.9983692,0.0000316545,0.0005491821,0.0004224567,0.0002220404,0.000405509],"domain_scores_gemma":[0.9991401,0.00009873824,0.0001326529,0.0002930396,0.0002677483,0.00006770979],"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":[0.00011202,0.00007740858,0.0001497012,0.002105693,0.0009608535,0.000005420954,0.00189616,0.2297219,0.7437326,0.0007109863,0.000649881,0.01987736],"study_design_scores_gemma":[0.004807937,0.0001524121,0.001101848,0.0007821227,0.00008572744,0.00001341618,0.0003599262,0.9790835,0.01024409,0.00006372567,0.002559083,0.00074614],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2023777,0.000493984,0.7904353,0.0001346578,0.001443527,0.001979189,0.00005151113,0.0004179112,0.002666299],"genre_scores_gemma":[0.9877087,0.00001214834,0.009297851,0.0001601271,0.0006674855,0.001116572,0.0001617849,0.00009385616,0.0007814316],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7853311,"threshold_uncertainty_score":0.9998521,"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."}}