{"id":"W2968482510","doi":"10.1101/730713","title":"Tradeoffs in optimal control capture patterns of human sensorimotor control and adaptation","year":2019,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Motor Control and Adaptation","field":"Neuroscience","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hotchkiss Brain Institute; Queen's University; University of Calgary","funders":"","keywords":"Adaptation (eye); Control (management); Movement (music); Diversity (politics); Internal model; Computer science; Dynamics (music); Motor control; Session (web analytics); Control theory (sociology); Movement control; Range (aeronautics); Psychology; Cognitive psychology; Physical medicine and rehabilitation; Artificial intelligence; Neuroscience; Engineering; Medicine","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002357824,0.0003103441,0.0005632425,0.0007159222,0.0002824902,0.001447997,0.0003914109,0.0006794967,0.001798908],"category_scores_gemma":[0.01956679,0.0003001916,0.0003023887,0.0003860864,0.0007786263,0.001132297,0.00110105,0.0005477702,0.0002181071],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000447322,"about_ca_system_score_gemma":0.0002243599,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001079696,"about_ca_topic_score_gemma":0.0008475501,"domain_scores_codex":[0.9987329,0.0004573188,0.0001043529,0.0003588581,0.0002054021,0.0001413153],"domain_scores_gemma":[0.9946728,0.003106556,0.0006024953,0.0009863633,0.0003301217,0.0003017326],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.004348023,0.0005880359,0.2052341,0.0007179122,0.00117515,0.0006217191,0.004461938,0.2227222,0.3838962,0.02878891,0.001795702,0.14565],"study_design_scores_gemma":[0.000112892,0.0008832514,0.4643464,0.0000565534,0.0001307609,0.0006149804,0.001088641,0.4620259,0.02209752,0.04718731,0.001309222,0.000146488],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9726246,0.0001529235,0.0256308,0.0001179115,0.000006537078,0.0000214099,0.00007752057,0.00006447252,0.001303793],"genre_scores_gemma":[0.9974726,0.00001595002,0.002269123,0.00001418884,0.000002440913,0.00002221753,0.00004506168,0.00001595881,0.0001423651],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002357824,"threshold_uncertainty_score":0.01246953,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02099626194112571,"score_gpt":0.2220447817320091,"score_spread":0.2010485197908834,"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."}}