{"id":"W3082854464","doi":"10.1109/access.2020.3020864","title":"Nonparametric Bootstrap Technique to Improve Positional Accuracy in Mobile Robots With Differential Drive Mechanism","year":2020,"lang":"en","type":"article","venue":"IEEE Access","topic":"Robotic Mechanisms and Dynamics","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; Cogmation Robotics (Canada)","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Odometry; Computer science; Mobile robot; Robot; Artificial intelligence; Trajectory; Computer vision; Nonparametric statistics; Angular velocity; Control theory (sociology); Mathematics; Statistics; Physics","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":[0.00004396229,0.0002297397,0.0002637669,0.0002015511,0.00003770649,0.0001341931,0.0004285511,0.0001215312,0.00009497137],"category_scores_gemma":[0.00001802963,0.0002110674,0.00004883535,0.0006808749,0.00001117074,0.000384144,0.00007332553,0.0002720943,0.00002679894],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007728677,"about_ca_system_score_gemma":0.00003049826,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002898173,"about_ca_topic_score_gemma":0.00002130408,"domain_scores_codex":[0.9988356,0.00001519162,0.0002540937,0.0003288793,0.0002431291,0.0003231702],"domain_scores_gemma":[0.9994416,0.00006340747,0.00004052176,0.0002030346,0.00004333897,0.000208075],"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":[0.00005712741,0.000067843,0.00006412183,0.00009182451,0.00003779174,0.00006533442,0.0001537411,0.8401809,0.1525018,0.00380576,0.0001150952,0.002858648],"study_design_scores_gemma":[0.00166453,0.0009790598,0.0018376,0.0001553167,0.00006174798,0.00003383203,0.00006613782,0.7079526,0.2778126,0.00816191,0.00004672939,0.001227933],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.153224,0.00001164631,0.8450933,0.00007968937,0.0002628289,0.0008638786,0.00002840067,0.0002101273,0.0002261105],"genre_scores_gemma":[0.9613376,0.0000143155,0.03771678,0.0002465383,0.0001345848,0.0004719441,0.00001571935,0.00005219622,0.0000103601],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8081135,"threshold_uncertainty_score":0.8607078,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01585659157420178,"score_gpt":0.26116864481411,"score_spread":0.2453120532399082,"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."}}