{"id":"W4387448055","doi":"10.1109/ojcsys.2023.3322906","title":"Training Reflexes Using Adaptive Feedforward Control","year":2023,"lang":"en","type":"article","venue":"IEEE Open Journal of Control Systems","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Feed forward; Disturbance (geology); Computer science; Control theory (sociology); Control engineering; Feedforward neural network; Controller (irrigation); Adaptive control; Control (management); Internal model; Reflex; Engineering; Artificial intelligence; Artificial neural network; Neuroscience; Psychology; Biology","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":[0.001783889,0.0001952747,0.0006992507,0.000260567,0.0002651083,0.0005280116,0.0006789565,0.00008004721,0.0000107842],"category_scores_gemma":[0.0003825384,0.0001497374,0.0001968582,0.0004552616,0.00006435892,0.0008252652,0.00003322992,0.0003074173,0.00004435426],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001026525,"about_ca_system_score_gemma":0.0001780283,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006059833,"about_ca_topic_score_gemma":0.000004328243,"domain_scores_codex":[0.9973932,0.0005493613,0.0008227447,0.0002611834,0.0005637392,0.0004097931],"domain_scores_gemma":[0.9977596,0.0006909111,0.0009686099,0.0001848263,0.0002126259,0.0001834308],"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":[0.001036658,0.00003184059,0.0001087434,0.0000193974,0.00009390356,0.0004151206,0.0002994045,0.09015036,0.9022318,0.001809554,0.0008269057,0.002976388],"study_design_scores_gemma":[0.01457256,0.001920712,0.0004860297,0.0008132465,0.0002535101,0.002521486,0.00181679,0.9610425,0.005692959,0.001434937,0.008779165,0.0006661472],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9285778,0.0002234424,0.04637124,0.001347743,0.01458208,0.002153773,0.0001359387,0.0001085768,0.006499361],"genre_scores_gemma":[0.9980335,0.00001555479,0.00003752498,0.000459292,0.0007604865,0.00001188006,2.937284e-7,0.00003373385,0.0006477619],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8965388,"threshold_uncertainty_score":0.6106113,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1568136008807832,"score_gpt":0.3394947360125162,"score_spread":0.182681135131733,"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."}}