{"id":"W2109302401","doi":"10.1162/neco.2008.04-07-507","title":"Sensitivity Derivatives for Flexible Sensorimotor Learning","year":2008,"lang":"en","type":"article","venue":"Neural Computation","topic":"Motor Control and Adaptation","field":"Neuroscience","cited_by":49,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University; York University; University of Toronto","funders":"","keywords":"Sensitivity (control systems); Computer science; Psychology; Artificial intelligence; Machine learning; Engineering","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":[0.00008461344,0.0000945806,0.0001089382,0.00005553977,0.0003917106,0.00002688626,0.00003150074,0.00002972384,0.000003892228],"category_scores_gemma":[0.0005339876,0.00009174167,0.00005546786,0.0001295961,0.0000462809,0.0002478855,0.00001247913,0.00009355461,0.00001755792],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001937364,"about_ca_system_score_gemma":0.00001687271,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001121558,"about_ca_topic_score_gemma":0.000001181622,"domain_scores_codex":[0.999114,0.0001782723,0.0001349998,0.000253456,0.0001561296,0.0001631494],"domain_scores_gemma":[0.9991344,0.0006182638,0.00009338241,0.00004581236,0.00006714401,0.00004102492],"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":[0.0001061792,0.00003199333,0.0005531108,0.00001425438,0.000002040743,0.0000177619,0.0005054532,0.0926821,0.8900979,0.0005397867,0.00006493491,0.0153845],"study_design_scores_gemma":[0.0006792595,0.0001885872,0.03516462,0.000004855397,0.00000422906,0.00005558047,0.00002754348,0.8841191,0.07840659,0.0004187448,0.0007943174,0.0001365945],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7976819,0.000004703322,0.2011372,0.0002501314,0.0001831751,0.0002847479,0.000003612172,0.000210793,0.0002437094],"genre_scores_gemma":[0.9975075,0.000003119294,0.001548253,0.0003422475,0.0001463937,0.0000167579,0.00001013887,0.00001288907,0.0004127003],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8116913,"threshold_uncertainty_score":0.3741117,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07841326198561792,"score_gpt":0.2942664919996806,"score_spread":0.2158532300140627,"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."}}