{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005834021,0.00006622556,0.0001099854,0.00003964124,0.00002869368,0.000004615266,0.00008476034,0.00002651518,0.0004260864],"category_scores_gemma":[0.000001672995,0.0000448832,0.00002172735,0.00002944818,0.0000345966,0.0001019825,0.0001185846,0.00005167326,0.0002242307],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003722298,"about_ca_system_score_gemma":3.758837e-7,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005172306,"about_ca_topic_score_gemma":0.00006709556,"domain_scores_codex":[0.9994704,0.00001986674,0.000152302,0.0001483036,0.00007218788,0.0001370101],"domain_scores_gemma":[0.9998084,0.00001113996,0.0000227933,0.0001424012,0.000001809162,0.00001340763],"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":[0.000006312183,0.00005412654,0.3809323,0.0000045557,0.00000429873,1.089354e-7,0.002061897,0.6139677,0.002562825,0.00002830699,0.00002987256,0.0003476887],"study_design_scores_gemma":[0.0001734085,0.00001645306,0.03729966,0.000005822078,0.000005557275,2.347175e-8,0.00006877392,0.955112,0.005650466,0.001536421,0.00007107772,0.00006037755],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9787441,0.000001462724,0.01555285,0.0002969503,0.0000361162,0.0001365136,7.034714e-7,0.00001126005,0.005220057],"genre_scores_gemma":[0.9988781,0.000001005673,0.0001382043,0.00004054243,0.000002924235,0.000008718645,0.000007207889,0.000004270354,0.0009190049],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3436326,"threshold_uncertainty_score":0.4665347,"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."}}