{"id":"W2121994657","doi":"10.1109/icnn.1997.611669","title":"CMNN: cooperative modular neural network","year":2002,"lang":"en","type":"article","venue":"Proceedings of International Conference on Neural Networks (ICNN'97)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Modular design; Benchmark (surveying); Computer science; Artificial neural network; Voting; Artificial intelligence; Range (aeronautics); Architecture; Routing (electronic design automation); Machine learning; Engineering; Programming language; Computer network","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":[0.001006224,0.0007785944,0.0006500652,0.0006271731,0.0003374604,0.0005253835,0.001884534,0.0008527007,0.003053604],"category_scores_gemma":[0.002355359,0.0002526394,0.0004366889,0.0007534827,0.0005535524,0.001117117,0.00116006,0.0007188719,0.0009484636],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006099255,"about_ca_system_score_gemma":0.0008036657,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003043355,"about_ca_topic_score_gemma":0.003665047,"domain_scores_codex":[0.9994837,0.0001255421,0.0000219185,0.0001255115,0.0001735411,0.00006973487],"domain_scores_gemma":[0.9993165,0.0001928567,0.00009390608,0.0001205084,0.0002336927,0.00004256692],"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":[0.000283002,0.0001312939,0.001904571,0.0002559128,0.0001553162,0.0001968916,0.00009332648,0.4442864,0.01508068,0.01854406,0.01115999,0.5079085],"study_design_scores_gemma":[0.00001549374,0.00007126433,0.0003760719,0.00000881537,0.00001579741,0.00005456553,0.00000655946,0.9865525,0.002411962,0.007459003,0.003017688,0.00001024757],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03247843,0.0008243013,0.9582288,0.0003423235,0.0001479655,0.0001090995,0.0002045204,0.002599164,0.005065405],"genre_scores_gemma":[0.7384631,0.0005537738,0.2511924,0.000385213,0.0001942697,0.0003609762,0.0007568562,0.0001450874,0.007948274],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003053604,"threshold_uncertainty_score":0.01021534,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04895050452239463,"score_gpt":0.2599312428033304,"score_spread":0.2109807382809357,"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."}}