{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001142467,0.0006401016,0.0008290953,0.0007859347,0.0005398394,0.001502387,0.001675425,0.001314636,0.001935821],"category_scores_gemma":[0.005585294,0.0004190069,0.0006640504,0.0008244696,0.001062831,0.001294867,0.001784355,0.0008930464,0.0004340897],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007547595,"about_ca_system_score_gemma":0.0006190708,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00219784,"about_ca_topic_score_gemma":0.001746049,"domain_scores_codex":[0.9994153,0.0001815882,0.00002752376,0.0001224643,0.0001649151,0.00008822828],"domain_scores_gemma":[0.9988235,0.0005026666,0.00008700367,0.0002497855,0.0002446279,0.00009230102],"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":[0.0001386068,0.000141287,0.002842707,0.0001152561,0.000209041,0.0005118488,0.0006776961,0.7584676,0.02629784,0.09123618,0.001695716,0.1176663],"study_design_scores_gemma":[0.00002317996,0.00007666647,0.0004098897,0.00001188342,0.00003174628,0.0001145399,0.00007337918,0.9734181,0.003235378,0.02014536,0.002440739,0.00001920041],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2327796,0.0004669716,0.7464909,0.0003350718,0.00007191656,0.0001431589,0.00004700162,0.0005176309,0.01914776],"genre_scores_gemma":[0.8890771,0.000248857,0.1026091,0.0001271716,0.00001701945,0.0002196752,0.00007710355,0.00006680104,0.007557057],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00219784,"threshold_uncertainty_score":0.006475925,"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."}}