{"id":"W4377964805","doi":"10.1123/mc.2023-0014","title":"Identifying Referent Control Variables Underlying Goal-Directed Arm Movements","year":2023,"lang":"en","type":"article","venue":"Motor Control","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Université de Montréal; Centre for Interdisciplinary Research in Rehabilitation","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Referent; Motor control; Physical medicine and rehabilitation; Kinematics; Electromyography; Psychology; Control theory (sociology); Computer science; Control (management); Neuroscience; Medicine; Artificial intelligence; Physics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000203348,0.0002354746,0.000324844,0.000299803,0.0002038997,0.00009266573,0.0001539465,0.00007831015,0.0001016662],"category_scores_gemma":[0.00006349082,0.0002357291,0.0001254451,0.00053028,0.00002002487,0.0001539953,0.00001866966,0.0001768817,0.00004453285],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009915727,"about_ca_system_score_gemma":0.00001072074,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004051222,"about_ca_topic_score_gemma":0.00001985905,"domain_scores_codex":[0.9985884,0.00004977851,0.000317855,0.0002438225,0.0002672399,0.0005328577],"domain_scores_gemma":[0.9993784,0.0001794698,0.00005342904,0.0002247551,0.00007177619,0.00009215858],"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":[0.0002803145,0.0002400646,0.01320571,0.0004611826,0.004987329,0.00004963343,0.001266043,0.01274312,0.8476291,0.003090083,0.02412904,0.09191842],"study_design_scores_gemma":[0.0130201,0.0002100325,0.6555444,0.0001801305,0.0002123543,0.000002742921,0.000567932,0.2940799,0.002355616,0.002488919,0.03016368,0.001174158],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7602384,0.002553482,0.1811952,0.001226389,0.005169187,0.004073755,0.0003194329,0.01962789,0.02559622],"genre_scores_gemma":[0.998553,0.0001342955,0.00009552947,0.0002531256,0.0001093111,0.0002736032,0.00002156127,0.00005050912,0.0005090761],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8452734,"threshold_uncertainty_score":0.9612753,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02758254994717685,"score_gpt":0.2503305181684173,"score_spread":0.2227479682212404,"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."}}