{"id":"W2091212769","doi":"10.1142/s0129065700000235","title":"COOPERATIVE COEVOLUTION OF NEURAL REPRESENTATIONS","year":2000,"lang":"en","type":"article","venue":"International Journal of Neural Systems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; CMC Microsystems","keywords":"Pattern recognition (psychology); Encoding (memory); Computer science; Artificial intelligence; Chromosome; Artificial neural network; Perceptron; Fitness function; Feature (linguistics); Set (abstract data type); Multilayer perceptron; Detector; Task (project management); Feature extraction; Genetic algorithm; Representation (politics); Machine learning; Biology; Gene","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":[0.0001566414,0.00009314497,0.0001820768,0.0001126263,0.00006094871,0.0001334443,0.0009616976,0.00003224298,0.00005083357],"category_scores_gemma":[0.00002528181,0.00007518018,0.0001208428,0.0002466932,0.00004902844,0.0007227414,0.00005140994,0.0001504857,0.00001415994],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004528561,"about_ca_system_score_gemma":0.00003859218,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008202219,"about_ca_topic_score_gemma":0.000003897166,"domain_scores_codex":[0.9984549,0.0001050425,0.0006508989,0.0001431879,0.0005280009,0.0001180114],"domain_scores_gemma":[0.9984786,0.0001213991,0.0004312345,0.0001871412,0.0007046982,0.00007691448],"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":[0.0002146964,0.0006083178,0.005912985,0.00002418598,0.0004453391,0.0002642675,0.001289638,0.7283025,0.03162932,0.09252217,0.02847162,0.110315],"study_design_scores_gemma":[0.001385253,0.0004251936,0.02011175,0.0001568983,0.00002809028,0.002485296,0.0001431534,0.9529757,0.001896362,0.001005103,0.01909887,0.000288358],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9278857,0.0008056021,0.05705813,0.007328032,0.003622338,0.0003296456,0.0000294071,0.00005228725,0.002888917],"genre_scores_gemma":[0.9982669,0.00006558034,0.0005204408,0.0001146702,0.0004698389,0.000006202937,0.000003267157,0.00000535115,0.0005477398],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2246732,"threshold_uncertainty_score":0.3065759,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02020053260670272,"score_gpt":0.2975792532595969,"score_spread":0.2773787206528942,"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."}}