{"id":"W2900437431","doi":"10.3390/w10111604","title":"Multi-Model Approaches for Improving Seasonal Ensemble Streamflow Prediction Scheme with Various Statistical Post-Processing Techniques in the Canadian Prairie Region","year":2018,"lang":"en","type":"article","venue":"Water","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":48,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University; Manitoba Hydro; University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Streamflow; Quantile; Predictability; Flood forecasting; Hydrological modelling; Environmental science; Calibration; Computer science; Bayesian probability; Meteorology; Bayesian inference; Climatology; Econometrics; Drainage basin; Statistics; Mathematics; Geography; 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":[0.0008484364,0.0004499189,0.0002956128,0.000375205,0.0004907383,0.0005339177,0.000718663,0.0003069329,0.000777997],"category_scores_gemma":[0.001593508,0.000244081,0.0004352634,0.0004410549,0.0001638858,0.0005902309,0.0004390372,0.0005937534,0.0001061326],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0013507,"about_ca_system_score_gemma":0.003809561,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.4531948,"about_ca_topic_score_gemma":0.4760586,"domain_scores_codex":[0.9997985,0.00003580669,0.00001545345,0.00004725006,0.00007177787,0.00003126009],"domain_scores_gemma":[0.999622,0.00006934083,0.00003066923,0.00004045845,0.0002195406,0.00001806884],"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":[0.00004471456,0.00005223147,0.006881339,0.00002305566,0.00008839867,0.00003588416,0.00005975783,0.8778198,0.005270392,0.001603508,0.000645182,0.1074758],"study_design_scores_gemma":[0.000002611774,0.000006394646,0.001274143,0.000001157636,0.000007595243,0.000001886879,0.000005959363,0.9976941,0.0006190266,0.0001354038,0.0002478839,0.000003878039],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.3456495,0.0004542106,0.6471234,0.0005510271,0.00007743301,0.0001095051,0.0004372698,0.001769272,0.003828545],"genre_scores_gemma":[0.8444968,0.0001863471,0.1532176,0.0000611137,0.00001801569,0.00005872417,0.000367844,0.00006581401,0.001527665],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.5468051,"threshold_uncertainty_score":0.9011139,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02610012905973363,"score_gpt":0.2294971571380644,"score_spread":0.2033970280783308,"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."}}