{"id":"W4245468176","doi":"10.1109/wsc.2004.1371455","title":"Simulation Input Updating Using Bayesian Techniques","year":2005,"lang":"en","type":"article","venue":"Proceedings of the 2004 Winter Simulation Conference, 2004.","topic":"Geotechnical Engineering and Analysis","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Bayesian probability; Quality (philosophy); Machine learning; Term (time); Data mining; Artificial intelligence","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":[0.006689337,0.001196421,0.001530568,0.002205061,0.0008916733,0.002529437,0.002920598,0.001668119,0.006679455],"category_scores_gemma":[0.0476402,0.001550212,0.001260642,0.00174264,0.0008887482,0.003841872,0.002359278,0.002815682,0.001621718],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001527301,"about_ca_system_score_gemma":0.002364283,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008322081,"about_ca_topic_score_gemma":0.00746762,"domain_scores_codex":[0.9946468,0.002573862,0.0003108112,0.000595297,0.001643541,0.0002297069],"domain_scores_gemma":[0.9801331,0.01296499,0.001017284,0.002089129,0.003552034,0.0002433826],"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":[0.0001197384,0.00008633114,0.00117733,0.00009401063,0.00008473518,0.0000800711,0.0001754088,0.8325809,0.001150911,0.05402509,0.002022741,0.1084027],"study_design_scores_gemma":[0.00002204405,0.0000185771,0.0001158505,0.00002033273,0.00001516279,0.00001961289,0.00001008873,0.9742167,0.0006356774,0.02277136,0.002138469,0.00001615029],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001798099,0.00005815167,0.9954051,0.0001025551,0.00002504667,0.00005039321,0.00004698589,0.000572675,0.001941072],"genre_scores_gemma":[0.2309351,0.0004871174,0.7629188,0.0001984806,0.0001203338,0.0005929313,0.0005253331,0.0006068788,0.003615035],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008322081,"threshold_uncertainty_score":0.03537703,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0149048426509726,"score_gpt":0.2474408331272101,"score_spread":0.2325359904762375,"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."}}