{"id":"W3011652201","doi":"10.48550/arxiv.2003.05992","title":"Comments on `Design and Implementation of Model-Predictive Control With Friction Compensation on an Omnidirectional Mobile Robot'","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Control Systems Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Omnidirectional antenna; Compensation (psychology); Model predictive control; Mobile robot; Control (management); Robot; Computer science; Control engineering; Control theory (sociology); Engineering; Artificial intelligence; Psychology; Telecommunications; Social psychology","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":[0.00009477699,0.0002526692,0.0003035303,0.0001811626,0.00008068012,0.00001955488,0.000110063,0.0001393308,0.000007693407],"category_scores_gemma":[0.000003637168,0.0002905283,0.00004189544,0.0001558548,0.0000306727,0.0002506136,0.00003122315,0.0002565687,0.000002516365],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003510161,"about_ca_system_score_gemma":0.00003685069,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004718525,"about_ca_topic_score_gemma":0.00001810169,"domain_scores_codex":[0.9989719,0.0001278405,0.0002053387,0.0004463443,0.0001118255,0.0001367464],"domain_scores_gemma":[0.9992629,0.00008176859,0.0002064552,0.0002306808,0.0001308915,0.00008735668],"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":[0.0005074991,0.00004243072,0.0004489066,0.00004975184,0.0001722211,0.000003609962,0.0002334657,0.9955295,0.0005971566,0.002145756,0.00002118898,0.0002485438],"study_design_scores_gemma":[0.002076248,0.000666951,0.001111764,0.00007247448,0.000130133,6.970319e-7,0.0002824485,0.9936619,0.000611962,0.001158088,0.000004206247,0.0002230772],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1069021,0.0000101365,0.8913124,0.00001352709,0.0001259327,0.001142643,0.00009853819,0.0001998822,0.0001948786],"genre_scores_gemma":[0.9977893,0.00004808114,0.00185881,0.00003004572,0.00004402383,0.0000216558,0.0001591643,0.00003918853,0.000009756387],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8908872,"threshold_uncertainty_score":0.9999547,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04127283109032696,"score_gpt":0.2010110995951151,"score_spread":0.1597382685047882,"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."}}