{"id":"W2978242027","doi":"10.1017/s0263574719001413","title":"Gait Optimization for Quadruped Rovers","year":2019,"lang":"en","type":"article","venue":"Robotica","topic":"Robotic Locomotion and Control","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Gait; Kinematics; Torque; Control theory (sociology); Computer science; Effect of gait parameters on energetic cost; Stability (learning theory); Simulation; Engineering; Gait analysis; Artificial intelligence; Control (management); Physical medicine and rehabilitation; Physics","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":[],"consensus_categories":[],"category_scores_codex":[0.0000519496,0.00007985539,0.0001124259,0.00003387385,0.00002122694,0.00002434567,0.00006898431,0.00005402216,0.0004700088],"category_scores_gemma":[0.00001249619,0.0000802225,0.0000542144,0.0000617652,0.00000576929,0.00007443476,0.000005405044,0.0000485592,0.0002577419],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003327554,"about_ca_system_score_gemma":0.000008721549,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001217628,"about_ca_topic_score_gemma":7.650688e-7,"domain_scores_codex":[0.9995508,0.000007086016,0.0001137209,0.00009753428,0.00007057931,0.000160323],"domain_scores_gemma":[0.999721,0.00004512288,0.00001195574,0.0001468247,0.00002755968,0.00004755081],"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":[0.00000495123,0.000007981852,0.00005995631,0.0000291322,0.00001819287,1.719405e-7,0.0000265415,0.9933387,0.0004111125,0.004340968,0.0008157608,0.0009465414],"study_design_scores_gemma":[0.0006811126,0.00002492173,0.0001102948,0.000008841091,0.00001324453,7.627377e-7,0.00001927301,0.9970273,0.0001318387,0.00008367025,0.001793641,0.0001050486],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0007976964,0.00003734649,0.9858016,0.0002725371,0.0005818571,0.0003794068,0.00000224531,0.0002662785,0.01186098],"genre_scores_gemma":[0.9603504,0.00001246263,0.03763487,0.000183587,0.00008915624,0.00003067888,0.00001992976,0.0000371646,0.001641785],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9595526,"threshold_uncertainty_score":0.5146267,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004507093029491961,"score_gpt":0.184532579347813,"score_spread":0.180025486318321,"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."}}