{"id":"W1531902512","doi":"","title":"Combining Competitive And Cooperative Coevolution For Training Cascade Neural Networks","year":2002,"lang":"en","type":"article","venue":"Genetic and Evolutionary Computation Conference","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Coevolution; Artificial neural network; Computer science; Cascade; Crossover; Artificial intelligence; Retraining; Evolutionary algorithm; Quality (philosophy); Machine learning; Ecology; Biology; 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":[0.00007558682,0.0001606715,0.0001750883,0.00005650863,0.0005483467,0.0001409732,0.0001544384,0.00006319967,0.00001065162],"category_scores_gemma":[0.00001431489,0.0001649592,0.00002874334,0.0001984515,0.0001694363,0.0002559881,0.00010321,0.00009695144,0.000002507198],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002138391,"about_ca_system_score_gemma":0.0000223487,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008986302,"about_ca_topic_score_gemma":0.000005847196,"domain_scores_codex":[0.998875,0.00007111581,0.0002403182,0.0004332193,0.0001166286,0.0002637029],"domain_scores_gemma":[0.9991893,0.0002814856,0.00009874074,0.0001118182,0.0001950803,0.0001236146],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001690542,0.00008400763,0.0009904656,0.00002619017,0.0000407868,0.000005189805,0.002502498,0.3085008,0.0002081456,0.4156864,0.002926556,0.2690121],"study_design_scores_gemma":[0.0004720318,0.0001483066,0.01829258,0.00002053261,0.000009925433,0.00008040472,0.0001788811,0.9745228,0.000003378049,0.005448326,0.000631332,0.0001914874],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03029134,0.001666868,0.9655606,0.00149236,0.0001542147,0.0003936893,0.000009958898,0.00009426186,0.0003367374],"genre_scores_gemma":[0.9556297,0.0001768727,0.04364736,0.0002683185,0.00009259602,0.00007647057,0.00002232926,0.000007590192,0.00007874348],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9253384,"threshold_uncertainty_score":0.6726841,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04332755254035137,"score_gpt":0.2507727391580069,"score_spread":0.2074451866176555,"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."}}