{"id":"W173424","doi":"","title":"Improving the efficiency of bayesian network based edas and their application in bioinformatics","year":2013,"lang":"en","type":"article","venue":"","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"EDAS; Bayesian network; Estimation of distribution algorithm; Computer science; Heuristic; Mathematical optimization; Probabilistic logic; Benchmark (surveying); Machine learning; Artificial intelligence; Bottleneck; Bayesian probability; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003367458,0.00007289607,0.0000845562,0.00003643231,0.00005755388,0.00008087302,0.0004032437,0.00003452162,0.000003588974],"category_scores_gemma":[0.00001317288,0.00004143324,0.00001659344,0.0002970116,0.00004473794,0.000218089,0.00008969125,0.00007635388,0.000005939437],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007126563,"about_ca_system_score_gemma":0.00004040248,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003165609,"about_ca_topic_score_gemma":0.00002134351,"domain_scores_codex":[0.9994003,0.00002514616,0.0002184663,0.0001189391,0.00007699936,0.0001601795],"domain_scores_gemma":[0.9993981,0.0001075208,0.00007780727,0.0003375665,0.00004576117,0.00003328125],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000002244057,0.00007016406,0.00321546,0.00006839557,0.000004483025,1.287111e-7,0.001898609,0.01339058,0.002028276,0.1060043,0.0002648254,0.8730525],"study_design_scores_gemma":[0.00007157473,0.00002285942,0.0007912429,0.00001158091,5.850598e-7,7.583129e-7,0.00005851622,0.9929607,0.0008206718,0.005193499,0.00001125602,0.00005678231],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01586787,0.00004868285,0.9823355,0.0005227368,0.00002579101,0.0001928769,1.790853e-7,0.00003784392,0.0009685126],"genre_scores_gemma":[0.9211364,0.000002936957,0.07848958,0.000328918,0.00001082863,0.00002059329,3.920836e-7,0.000002178533,0.000008123327],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9795701,"threshold_uncertainty_score":0.1689598,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008843379996336491,"score_gpt":0.2032401138390413,"score_spread":0.1943967338427048,"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."}}