{"id":"W4389206100","doi":"10.22215/etd/2023-15848","title":"Precision Robotic Machining: Modelling and Control Innovations for Improved Performance","year":2023,"lang":"en","type":"dissertation","venue":"","topic":"Robotic Mechanisms and Dynamics","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Workspace; Machining; Control engineering; Artificial neural network; Computer science; Robot; Kinematics; Reliability (semiconductor); Process (computing); Engineering; Artificial intelligence; Mechanical engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005863156,0.0006945533,0.0005037841,0.0002847131,0.0002664286,0.0009901603,0.001080401,0.0008577936,0.001822736],"category_scores_gemma":[0.0007684284,0.000459212,0.0005832231,0.0004958584,0.000658416,0.001236268,0.0006605264,0.001252766,0.0005784136],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006332959,"about_ca_system_score_gemma":0.0007666883,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001605997,"about_ca_topic_score_gemma":0.001171435,"domain_scores_codex":[0.9995914,0.00004117003,0.00001928819,0.00007213104,0.0002525475,0.00002352407],"domain_scores_gemma":[0.9997866,0.0000527259,0.00004492642,0.00005083,0.00005673357,0.000008210112],"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":[0.00006341047,0.00007188918,0.0006130958,0.0006382561,0.00004570069,0.0001100458,0.0001716305,0.5678466,0.06429046,0.117914,0.003296678,0.2449382],"study_design_scores_gemma":[0.00001931863,0.0001258695,0.0005481218,0.00006438488,0.00002534717,0.0001208814,0.0000180596,0.9399294,0.01065274,0.01844518,0.03002018,0.00003050415],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006727278,0.002273189,0.9833342,0.000330077,0.00008614845,0.00003363656,0.00004523714,0.0004934241,0.006676791],"genre_scores_gemma":[0.4917137,0.00998579,0.4834141,0.0002356298,0.0002383887,0.0002779757,0.0002972363,0.0002090739,0.01362813],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001822736,"threshold_uncertainty_score":0.006097674,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01570681014915833,"score_gpt":0.2336709980206157,"score_spread":0.2179641878714574,"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."}}