{"id":"W2969844591","doi":"10.3390/w11081707","title":"Inter-Comparison of Different Bayesian Model Averaging Modifications in Streamflow Simulation","year":2019,"lang":"en","type":"article","venue":"Water","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":40,"is_retracted":false,"has_abstract":true,"ca_institutions":"United Nations University Institute for Water, Environment, and Health; McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Streamflow; Reliability (semiconductor); Computer science; Probabilistic logic; Transformation (genetics); Bayesian probability; Variance (accounting); Standard deviation; Maximization; Context (archaeology); Bayesian inference; Data mining; Statistics; Econometrics; Mathematics; Mathematical optimization; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.007762413,0.0006418765,0.0007000366,0.0008186346,0.0005043907,0.0008789662,0.0009126968,0.0007361142,0.000546246],"category_scores_gemma":[0.01764336,0.0003121489,0.0007198423,0.0007795506,0.0003728045,0.001580069,0.000872146,0.0007180955,0.0001140567],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007817154,"about_ca_system_score_gemma":0.001044977,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01023394,"about_ca_topic_score_gemma":0.009569595,"domain_scores_codex":[0.997017,0.001752224,0.000223683,0.0002643955,0.000595912,0.0001467451],"domain_scores_gemma":[0.9933496,0.003955087,0.0004156412,0.0008854835,0.001250436,0.0001436749],"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":[0.0004134109,0.0002170758,0.01337815,0.0001024742,0.0003547217,0.00004267401,0.0001083734,0.8984763,0.004722612,0.003968743,0.0004149932,0.0778005],"study_design_scores_gemma":[0.00002027005,0.000151769,0.004868031,0.00001307003,0.000061502,0.00001316151,0.00002860763,0.9900486,0.003253366,0.001106631,0.0004115559,0.0000234418],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7177058,0.0007476211,0.276187,0.0003034337,0.0001004225,0.000154625,0.0002729741,0.0007902728,0.003737797],"genre_scores_gemma":[0.9448147,0.0002120384,0.05430757,0.00004209781,0.0000178763,0.00007696936,0.0002220667,0.00006227048,0.0002444423],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01023394,"threshold_uncertainty_score":0.0410521,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01651818953451805,"score_gpt":0.2578032023130301,"score_spread":0.2412850127785121,"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."}}