{"id":"W2806045912","doi":"10.1139/tcsme-2017-0143","title":"The adaptable amphibious wheel-legged robot","year":2018,"lang":"en","type":"article","venue":"Transactions of the Canadian Society for Mechanical Engineering","topic":"Robotic Locomotion and Control","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Shanghai Municipal Education Commission; National Natural Science Foundation of China; Natural Science Foundation of Shanghai","keywords":"Kinematics; Trajectory; Robot; Climb; Gait; Computer science; Mechanism (biology); Control theory (sociology); Simulation; Robot kinematics; Control engineering; Engineering; Mobile robot; Artificial intelligence; Control (management)","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":[0.00007382526,0.000320634,0.0002851482,0.0003124823,0.0002752135,0.0002129598,0.0006681157,0.000302713,0.001261556],"category_scores_gemma":[0.00008465017,0.000129231,0.0002000015,0.000178814,0.0002449873,0.0003377238,0.0004954349,0.0002321882,0.0003590911],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001133031,"about_ca_system_score_gemma":0.0001640987,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005166683,"about_ca_topic_score_gemma":0.000683306,"domain_scores_codex":[0.9999429,0.000007111808,0.000003150388,0.00001915079,0.00001920386,0.00000854893],"domain_scores_gemma":[0.9999379,0.000005621584,0.00001392587,0.00001240371,0.00001451802,0.00001558782],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002532802,0.0001167605,0.006780614,0.0006340197,0.00008657773,0.001557112,0.0002902804,0.08105698,0.4301375,0.03132489,0.005037341,0.4427246],"study_design_scores_gemma":[0.0002080579,0.002936042,0.02144217,0.0001511618,0.0002147258,0.005352802,0.0002627486,0.74029,0.05567819,0.01056664,0.1626547,0.0002426845],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2978837,0.00218053,0.677965,0.0001934085,0.0003226137,0.0001875384,0.0002094266,0.001964628,0.01909312],"genre_scores_gemma":[0.8622114,0.0006698237,0.1253834,0.00009696894,0.00005340435,0.0001716903,0.0001842433,0.0000204335,0.01120865],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001261556,"threshold_uncertainty_score":0.004220366,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007920404339553757,"score_gpt":0.1817783227751215,"score_spread":0.1738579184355677,"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."}}