{"id":"W1999861638","doi":"10.1145/2464576.2464590","title":"Dynamic memory for robot control via delay neural networks","year":2013,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Robot; Task (project management); Coincidence; Artificial neural network; Transmission (telecommunications); Limit (mathematics); Coincidence detection in neurobiology; Transmission delay; Control (management); Artificial intelligence; Real-time computing; Control theory (sociology); 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001739343,0.000274029,0.0001490275,0.0001655426,0.0001700718,0.0003073229,0.0004233186,0.0002806551,0.001191845],"category_scores_gemma":[0.0006849585,0.0001051667,0.0001673357,0.0001731095,0.0004920358,0.0005540053,0.0003380705,0.0004045929,0.00008982972],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004474914,"about_ca_system_score_gemma":0.0002779822,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001200879,"about_ca_topic_score_gemma":0.001624935,"domain_scores_codex":[0.9999566,0.000007708541,0.000002783581,0.00001234317,0.00001401174,0.00000648998],"domain_scores_gemma":[0.999864,0.00007641054,0.000019232,0.0000152179,0.00001678008,0.000008396319],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001799731,0.0000449787,0.0005447579,0.0001205579,0.00004070328,0.0001524067,0.0000928693,0.6042938,0.0828001,0.2412106,0.0004863382,0.07003289],"study_design_scores_gemma":[0.00001313848,0.0000562417,0.00008871009,0.000004983001,0.000007926378,0.00002778866,0.000004740044,0.9550828,0.007988863,0.035733,0.0009838321,0.00000804318],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07288396,0.0004278977,0.9219764,0.0001587966,0.00005025239,0.00002206183,0.00002924667,0.0002314459,0.004219933],"genre_scores_gemma":[0.9356287,0.0002728822,0.06123648,0.00004449623,0.0000187596,0.00006541526,0.00002183206,0.00002787643,0.002683616],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001200879,"threshold_uncertainty_score":0.003987134,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00615277322375553,"score_gpt":0.2090423750509073,"score_spread":0.2028896018271518,"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."}}