{"id":"W2787180295","doi":"10.1109/ssci.2017.8285173","title":"An adaptive spiking neural controller for flapping insect-scale robots","year":2017,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Reservoir Computing","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Flapping; Robot; Control theory (sociology); Controller (irrigation); Computer science; Artificial neural network; Scale (ratio); Spiking neural network; Power (physics); Control engineering; Adaptive control; Work (physics); Artificial intelligence; Control (management); Engineering; Aerospace engineering; Wing","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":[0.0002714495,0.0001574466,0.0002253807,0.00004440983,0.001210645,0.0009394732,0.001677397,0.00005908225,0.000003610839],"category_scores_gemma":[0.00002345705,0.000120184,0.0001046528,0.00005325897,0.00004760127,0.001038793,0.0004235891,0.0001384236,0.000005610583],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000164665,"about_ca_system_score_gemma":0.00002019972,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001217272,"about_ca_topic_score_gemma":0.0001110442,"domain_scores_codex":[0.9986779,0.0000412466,0.0002035148,0.0004650206,0.0001628339,0.000449464],"domain_scores_gemma":[0.9987261,0.000112686,0.0001757135,0.0007412327,0.0001084137,0.0001358359],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001826356,0.0001778012,0.003314917,0.00003174226,0.00008936263,0.00005323537,0.0007497665,0.1918238,0.009646506,0.04757892,0.003065994,0.7432853],"study_design_scores_gemma":[0.0006931594,0.0002254015,0.004280511,0.00002089404,0.000003547415,0.000008303065,0.00002113569,0.9922444,0.0003521644,0.001470254,0.0004896569,0.0001905416],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06824973,0.00005053043,0.926801,0.00108629,0.001079154,0.0003605328,6.615536e-7,0.0001947986,0.002177301],"genre_scores_gemma":[0.9428003,0.000001866986,0.05573639,0.0004955982,0.0006880254,0.00001578316,6.958807e-7,0.00001150474,0.0002498573],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8745505,"threshold_uncertainty_score":0.9311424,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05078439379659973,"score_gpt":0.2910021836756833,"score_spread":0.2402177898790836,"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."}}