{"id":"W4232646195","doi":"10.32920/ryerson.14652885.v1","title":"A novel developmental genetic programming methodology for mathematical modeling and neuroevolution","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Neuroevolution; Symbolic regression; Genetic programming; Computer science; Artificial intelligence; Artificial neural network; Pairwise comparison; Representation (politics); Genetic algorithm; Set (abstract data type); Interpretation (philosophy); Theoretical computer science; Machine learning; Programming language","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":[0.0006638159,0.0005343273,0.0003676628,0.0005400669,0.0003861825,0.0007288564,0.001229018,0.0006531339,0.002282455],"category_scores_gemma":[0.001448041,0.000284065,0.0007831622,0.0004789399,0.001060975,0.0006789096,0.001125354,0.001803962,0.0004657992],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007475495,"about_ca_system_score_gemma":0.0008278665,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009669335,"about_ca_topic_score_gemma":0.0009710061,"domain_scores_codex":[0.9996035,0.0001368844,0.00001732728,0.0000706417,0.0001490115,0.00002276456],"domain_scores_gemma":[0.9997221,0.0001579749,0.00002647024,0.00003147157,0.00004460743,0.000017436],"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":[0.00001786844,0.00003207524,0.0002966123,0.0001479405,0.00003332919,0.0001335597,0.0001778249,0.2150596,0.01478245,0.6552055,0.001803724,0.1123094],"study_design_scores_gemma":[0.00001489756,0.0000575885,0.00008970194,0.00004127503,0.00001956131,0.0001834002,0.00002563744,0.84044,0.006120202,0.1206699,0.03231635,0.00002140598],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0009372685,0.00005685857,0.9973569,0.00006306648,0.00001703421,0.0000138527,0.00001135366,0.00006735009,0.001476365],"genre_scores_gemma":[0.05219379,0.000266685,0.9441717,0.0001103347,0.00002627061,0.0001906961,0.00005584277,0.000104658,0.002879966],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002282455,"threshold_uncertainty_score":0.007635534,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1301289569377599,"score_gpt":0.3282239170783256,"score_spread":0.1980949601405657,"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."}}