{"id":"W3008907057","doi":"10.1152/jn.00696.2019","title":"Generalizing movement patterns following shoulder fixation","year":2020,"lang":"en","type":"article","venue":"Journal of Neurophysiology","topic":"Motor Control and Adaptation","field":"Neuroscience","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Government of Canada","keywords":"Elbow; Forearm; Orientation (vector space); Shoulder joint; Physical medicine and rehabilitation; Rotation (mathematics); Torque; Reduction (mathematics); Fixation (population genetics); Generalization; Computer science; Mathematics; Artificial intelligence; Medicine; Physics; Geometry; Anatomy; Mathematical analysis","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":[0.0002057214,0.0002340227,0.0002359074,0.0002048328,0.00009406271,0.0002469586,0.0002274186,0.0003247921,0.001340262],"category_scores_gemma":[0.001413579,0.0001568396,0.0002124581,0.00007621206,0.0002755104,0.0002077296,0.0004501608,0.0004191272,0.0002278694],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001209546,"about_ca_system_score_gemma":0.0001027363,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006740832,"about_ca_topic_score_gemma":0.001054555,"domain_scores_codex":[0.9998329,0.00001953386,0.00001372097,0.00006532047,0.00004039077,0.00002818238],"domain_scores_gemma":[0.9996087,0.0001103466,0.00008931987,0.0001043375,0.00004990957,0.00003736105],"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":[0.0001036048,0.00003993349,0.002672369,0.00004129158,0.00001787149,0.00005813924,0.00007937141,0.001325933,0.9752142,0.0000405393,0.00005032775,0.0203564],"study_design_scores_gemma":[0.00005851194,0.002588242,0.6598607,0.000040486,0.00004347176,0.00109376,0.0002060047,0.02641134,0.3067464,0.00118404,0.001720057,0.00004697058],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9850209,0.0001484418,0.01339706,0.00005052842,0.00001232345,0.00003395354,0.00007132265,0.0001702077,0.001095365],"genre_scores_gemma":[0.9968807,0.00007173143,0.002077457,0.00003660721,0.000005140785,0.00002005263,0.0001195423,0.00003034095,0.0007585827],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001340262,"threshold_uncertainty_score":0.00448364,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06092648684478259,"score_gpt":0.2804010827572923,"score_spread":0.2194745959125097,"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."}}