{"id":"W2936671355","doi":"10.1016/j.humov.2019.03.009","title":"The influence of robotic guidance on error detection and correction mechanisms","year":2019,"lang":"en","type":"article","venue":"Human Movement Science","topic":"Motor Control and Adaptation","field":"Neuroscience","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"Ford Motor Company (Canada); University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Foundation for Innovation; Ontario Research Foundation","keywords":"Computer science; Artificial intelligence; Computer vision","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":[0.00047721,0.0003764896,0.0002339203,0.0003334606,0.0003125762,0.001164666,0.0003719809,0.0005934895,0.002170907],"category_scores_gemma":[0.007999441,0.0002770846,0.0002447666,0.0002026874,0.0007094902,0.0007815198,0.0004138819,0.0004059571,0.0003147751],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003457018,"about_ca_system_score_gemma":0.0005429726,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001606226,"about_ca_topic_score_gemma":0.001356205,"domain_scores_codex":[0.9995518,0.0001212326,0.0000212009,0.00008485099,0.0001601315,0.00006069084],"domain_scores_gemma":[0.9976223,0.001512745,0.0002502092,0.0002069202,0.00029441,0.000113352],"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":[0.002248619,0.0002960009,0.007433177,0.0003306221,0.0001780358,0.0005354098,0.0005147133,0.07136972,0.6740382,0.03824807,0.001310036,0.2034973],"study_design_scores_gemma":[0.0002604214,0.001788963,0.203491,0.0001201573,0.0002676678,0.001657441,0.0004330059,0.5267005,0.1740845,0.08241116,0.008600322,0.0001849248],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8809961,0.002647329,0.08534843,0.0008237162,0.0003008663,0.00003285586,0.0001215488,0.0004249465,0.02930419],"genre_scores_gemma":[0.9946021,0.0003670201,0.003879914,0.00003233936,0.0000362851,0.000007854241,0.00002043169,0.0000550077,0.0009991991],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002170907,"threshold_uncertainty_score":0.007262468,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02044106775695765,"score_gpt":0.2566542145746485,"score_spread":0.2362131468176909,"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."}}