{"id":"W2106108252","doi":"10.1152/jn.00883.2004","title":"Generalization of Motor Learning Based on Multiple Field Exposures and Local Adaptation","year":2005,"lang":"en","type":"article","venue":"Journal of Neurophysiology","topic":"Motor Control and Adaptation","field":"Neuroscience","cited_by":115,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Workspace; Generalization; Torque; Computer science; Transfer of learning; Dynamics (music); Motor learning; Adaptation (eye); Artificial intelligence; Physics; Mathematics; Psychology; Mathematical analysis; Acoustics; Neuroscience; Optics; Robot","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":[0.00003631443,0.00006366584,0.0001390429,0.0001055831,0.00004733199,0.000006970632,0.00006297957,0.00003646857,0.00001366996],"category_scores_gemma":[0.0009580687,0.00005138265,0.00004619452,0.00005870847,0.0000376122,0.0001020599,0.000008261882,0.0001336417,0.000001528394],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007722976,"about_ca_system_score_gemma":0.00002049028,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005615898,"about_ca_topic_score_gemma":0.000001259036,"domain_scores_codex":[0.9992208,0.0002099544,0.0002608644,0.000102065,0.0001290364,0.00007726919],"domain_scores_gemma":[0.9989638,0.0005890047,0.0002986517,0.00005068898,0.00006323264,0.00003458835],"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":[0.0002781507,0.00003071073,0.00004397297,0.000004185517,9.336782e-7,0.000004104609,0.00005205396,0.3765742,0.6129523,0.00005645505,0.000002908336,0.009999963],"study_design_scores_gemma":[0.0008686897,0.002453185,0.02684266,0.00001409966,0.000007555386,0.00001177584,0.00002175955,0.9205097,0.04851278,0.00004803185,0.0006599812,0.00004977434],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9589682,0.00001087129,0.04039823,0.0004023691,0.000131783,0.00005255863,8.990436e-7,0.000006092688,0.00002896453],"genre_scores_gemma":[0.9983417,0.00003698619,0.0004111324,0.001012096,0.0001615064,9.856802e-7,4.53203e-7,0.000006633739,0.00002853113],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5644395,"threshold_uncertainty_score":0.2095324,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02613664952276879,"score_gpt":0.2446800170504198,"score_spread":0.2185433675276511,"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."}}