{"id":"W2089133220","doi":"10.1016/j.camwa.2009.12.039","title":"Bayesian network modeling for evolutionary genetic structures","year":2010,"lang":"en","type":"article","venue":"Computers & Mathematics with Applications","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":false,"ca_institutions":"York University","funders":"","keywords":"Artificial intelligence; Computer science; Bayesian network; Machine learning; Genetic algorithm; Artificial neural network; Complement (music); Evolutionary algorithm; Benchmark (surveying); Data mining; Biology","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":[0.005337286,0.00108018,0.002491296,0.002544294,0.00123115,0.002809643,0.003947971,0.003812616,0.006827572],"category_scores_gemma":[0.03291475,0.001615304,0.001913427,0.002867656,0.002755194,0.006653436,0.0018754,0.004219259,0.0009793297],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003519597,"about_ca_system_score_gemma":0.002055565,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02125298,"about_ca_topic_score_gemma":0.01884115,"domain_scores_codex":[0.997965,0.001188029,0.00007789536,0.0003687807,0.0002742131,0.000126061],"domain_scores_gemma":[0.9831103,0.01463997,0.0008046275,0.0004619962,0.0006655657,0.0003174644],"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":[0.00003064551,0.00002704246,0.0005514002,0.00006896136,0.00005967937,0.00005186221,0.0001214327,0.5932405,0.0001537808,0.3933582,0.001212016,0.01112448],"study_design_scores_gemma":[0.00000975765,0.000003198042,0.00008832475,0.00001212984,0.00001219994,0.00001302599,0.000008198731,0.7304623,0.0000289752,0.2687116,0.0006402286,0.00001010372],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005557276,0.0005765808,0.9910476,0.0007495458,0.00003449408,0.00002040918,0.0001740124,0.00008449492,0.001755583],"genre_scores_gemma":[0.5454765,0.005234292,0.4247558,0.0006332191,0.0005015021,0.0006634656,0.001562761,0.0003195144,0.02085304],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02125298,"threshold_uncertainty_score":0.04225856,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01597825083902822,"score_gpt":0.2472005646711959,"score_spread":0.2312223138321676,"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."}}