{"id":"W2971761264","doi":"10.1523/jneurosci.0770-19.2019","title":"Robust Control in Human Reaching Movements: A Model-Free Strategy to Compensate for Unpredictable Disturbances","year":2019,"lang":"en","type":"article","venue":"Journal of Neuroscience","topic":"Motor Control and Adaptation","field":"Neuroscience","cited_by":84,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary; Queen's University","funders":"","keywords":"Internal model; Perturbation (astronomy); Computer science; Motor learning; Motor control; Control theory (sociology); Adaptation (eye); Cognitive psychology; Psychology; Neuroscience; Control (management); Artificial intelligence; 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":[0.0005445226,0.0001378473,0.0003006765,0.0002141854,0.00014934,0.0001601514,0.0008305186,0.00002787154,0.000004007086],"category_scores_gemma":[0.0006405507,0.0001121531,0.0000845532,0.0003150378,0.00005361748,0.0008626722,0.00005666416,0.0002146943,0.000001886049],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007156372,"about_ca_system_score_gemma":0.00008450291,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001511284,"about_ca_topic_score_gemma":0.00001385855,"domain_scores_codex":[0.9981185,0.000090179,0.0005426573,0.0003457952,0.0005449593,0.0003578731],"domain_scores_gemma":[0.9989647,0.0001772426,0.0003980259,0.0002369397,0.00008178065,0.0001413048],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008500327,0.00006262861,0.000548761,0.000007876855,4.740397e-7,0.000009342326,0.00007736325,0.3197349,0.6777866,0.001440389,0.00004632902,0.0002003113],"study_design_scores_gemma":[0.004473525,0.001665833,0.01865152,0.00009835425,0.000009729584,0.00002763467,0.00006516072,0.9655657,0.004409377,0.004105078,0.000716215,0.0002118943],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9232632,0.00001980554,0.07433788,0.0005533237,0.0006067688,0.0006162639,0.0000403236,0.00001496738,0.0005474603],"genre_scores_gemma":[0.9962651,0.000005965919,0.0003784334,0.002735077,0.00006548454,0.00001117042,1.984339e-7,0.00001169153,0.0005269086],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6733773,"threshold_uncertainty_score":0.457347,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07225941133347658,"score_gpt":0.2851529808822636,"score_spread":0.212893569548787,"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."}}