{"id":"W4253480471","doi":"10.22215/etd/2009-09227","title":"Learning the neuron functions within neural networks based on genetic programming","year":2009,"lang":"en","type":"dissertation","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Library and Archives Canada","funders":"Universitat Politècnica de Catalunya","keywords":"Genetic programming; Artificial neural network; Computer science; Artificial intelligence; Humanities; Art","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":[0.0005474428,0.0003255491,0.0004337053,0.0002836388,0.0002254688,0.0008079292,0.0007788338,0.0006926588,0.001193392],"category_scores_gemma":[0.002079446,0.0002560682,0.0003886903,0.0003594394,0.0005371896,0.0008018616,0.0004319694,0.001059421,0.0002697187],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007114534,"about_ca_system_score_gemma":0.0005604473,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00448141,"about_ca_topic_score_gemma":0.005131231,"domain_scores_codex":[0.9998591,0.00004303646,0.000006720726,0.00002775105,0.00004351736,0.00001997187],"domain_scores_gemma":[0.9996417,0.0002118224,0.00002756614,0.00002691273,0.00007253712,0.00001949735],"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":[0.00003368847,0.00003254278,0.0003923782,0.00002387875,0.00002598079,0.00002611255,0.00004850131,0.9240696,0.003023518,0.01496665,0.000371301,0.05698576],"study_design_scores_gemma":[0.000003332471,0.000008371554,0.00004539478,0.00000364036,0.000003949912,0.000003939228,0.000003182387,0.9956731,0.0006228882,0.003405518,0.0002249828,0.000001707855],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0550426,0.0004122172,0.9385636,0.0003177059,0.0000701693,0.00003942727,0.00001702746,0.0002884529,0.005248817],"genre_scores_gemma":[0.6491877,0.0007127895,0.341658,0.0001171801,0.00006045096,0.0001152135,0.0000653243,0.000161752,0.007921553],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00448141,"threshold_uncertainty_score":0.008910656,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009179212263049779,"score_gpt":0.2353330484487959,"score_spread":0.2261538361857462,"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."}}