{"id":"W2087905578","doi":"10.1002/scj.1166","title":"Composition and decomposition learning of reaching movements under altered environments: An examination of the multiplicity of internal models","year":2002,"lang":"en","type":"article","venue":"Systems and Computers in Japan","topic":"Motor Control and Adaptation","field":"Neuroscience","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Kinematics; Transformation (genetics); Coordinate system; Computer science; Artificial intelligence; Physics; Classical mechanics","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":[0.000158986,0.00005956283,0.0001258503,0.00005870326,0.00004544945,0.00001268689,0.00006536489,0.00002502918,5.342193e-7],"category_scores_gemma":[0.000004490022,0.00004922319,0.00001671259,0.00004235241,0.00004942065,0.0001910659,0.00003648687,0.00006158188,4.105465e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000209408,"about_ca_system_score_gemma":0.000001009617,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001905972,"about_ca_topic_score_gemma":0.000003436859,"domain_scores_codex":[0.9991387,0.000260982,0.0002611155,0.0001379378,0.0001430865,0.00005824896],"domain_scores_gemma":[0.9995966,0.00005881586,0.000243923,0.0000747123,0.000008294972,0.00001764461],"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":[0.00001941244,0.0001384279,0.003689795,0.00007886589,0.000006560852,1.640912e-7,0.003656522,0.06842674,0.9064476,0.002240961,1.718225e-7,0.01529478],"study_design_scores_gemma":[0.0005636562,0.0000997021,0.1023226,0.0002083916,0.000003685205,0.000003468464,0.0002600889,0.8898201,0.006567316,0.000110497,7.071622e-7,0.00003979794],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9358801,0.00003182419,0.06373999,0.000006080138,0.00008297792,0.0001782109,0.00000305302,0.000003349205,0.00007439918],"genre_scores_gemma":[0.9997762,0.000011819,0.0001672044,0.00001790123,0.00001141728,0.000003476429,0.000001317895,0.000003732226,0.000006862857],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8998803,"threshold_uncertainty_score":0.2007263,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03068912266222302,"score_gpt":0.2355015982099236,"score_spread":0.2048124755477006,"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."}}