{"id":"W2809905232","doi":"10.1139/tcsme-2017-0126","title":"Ground mobile Bennett mechanism","year":2018,"lang":"en","type":"article","venue":"Transactions of the Canadian Society for Mechanical Engineering","topic":"Robotic Locomotion and Control","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Mechanism (biology); Kinematics; Computer science; Gait; Actuator; Simulation; Control theory (sociology); Artificial intelligence; Physics; Classical mechanics; Control (management); Physical medicine and rehabilitation","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001965483,0.0004815091,0.0004435255,0.0007030559,0.0003934754,0.0004818279,0.00129917,0.0005450025,0.006303729],"category_scores_gemma":[0.0003867466,0.0002621456,0.0002691238,0.0003590793,0.0004941443,0.0008263694,0.0009851852,0.0004057687,0.001188351],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001470329,"about_ca_system_score_gemma":0.0003201136,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002317571,"about_ca_topic_score_gemma":0.0003273804,"domain_scores_codex":[0.9996873,0.00003287046,0.00001727462,0.00007296408,0.0001404653,0.00004906141],"domain_scores_gemma":[0.9997911,0.00002423676,0.00004832018,0.000048353,0.00004405585,0.00004399695],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005772526,0.000133467,0.003476624,0.0008271025,0.00009398556,0.002057185,0.0002359032,0.01544096,0.5192204,0.07523851,0.002945481,0.3797533],"study_design_scores_gemma":[0.0006307316,0.009285772,0.02145911,0.0005571853,0.0002761937,0.02212905,0.0006355942,0.255656,0.3714816,0.04214038,0.2752304,0.0005180089],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3514244,0.005243218,0.5996771,0.0004356367,0.001051223,0.0002607269,0.0002369024,0.002308835,0.03936203],"genre_scores_gemma":[0.9064757,0.0006567201,0.08106771,0.00008365096,0.00005080945,0.00005107398,0.00009805077,0.00005443734,0.01146183],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006303729,"threshold_uncertainty_score":0.02108806,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007061597338247002,"score_gpt":0.1857921311001078,"score_spread":0.1787305337618608,"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."}}