{"id":"W3038745890","doi":"10.1038/s41598-020-67901-x","title":"Inverse optimal control with time-varying objectives: application to human jumping movement analysis","year":2020,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"University of Waterloo; Mitacs","keywords":"Computer science; Kinematics; Trajectory; Jump; Inverse kinematics; Inverse dynamics; Internal model; Control theory (sociology); Task (project management); Jumping; Artificial intelligence; Robot; Control (management); Engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000504897,0.0005202474,0.0005889702,0.0006131189,0.0002440075,0.0005265576,0.0002637181,0.0005345833,0.0009544051],"category_scores_gemma":[0.00188851,0.0002274412,0.0004516067,0.0004322022,0.0003664059,0.0002903936,0.0004381592,0.00056655,0.00009336825],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002437575,"about_ca_system_score_gemma":0.000507401,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008554305,"about_ca_topic_score_gemma":0.005789565,"domain_scores_codex":[0.9998374,0.0000351164,0.00001142397,0.00004662134,0.00004934669,0.00002003284],"domain_scores_gemma":[0.9995196,0.0003364549,0.0000481521,0.00002423714,0.00005590425,0.00001567816],"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":[0.0001214386,0.0001054262,0.001791733,0.0002190746,0.00007602591,0.0001831775,0.0001534889,0.8722236,0.01152049,0.003273658,0.0005875356,0.1097444],"study_design_scores_gemma":[0.000005127713,0.00002982553,0.001131994,0.000005960568,0.000004879613,0.00001998242,0.00001949218,0.996401,0.0008899937,0.001172555,0.0003123289,0.000006825976],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0962422,0.0004324497,0.9011632,0.00013514,0.00001927398,0.00005263641,0.0001223616,0.0003495185,0.001483222],"genre_scores_gemma":[0.8519874,0.0002536031,0.1455165,0.00003513857,0.00002536402,0.0001141423,0.0002920909,0.00006237553,0.001713418],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008554305,"threshold_uncertainty_score":0.01700902,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007915249562731597,"score_gpt":0.2151059921749976,"score_spread":0.207190742612266,"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."}}