{"id":"W2905216752","doi":"10.1029/2018wr023205","title":"A Stochastic Data‐Driven Ensemble Forecasting Framework for Water Resources: A Case Study Using Ensemble Members Derived From a Database of Deterministic Wavelet‐Based Models","year":2018,"lang":"en","type":"article","venue":"Water Resources Research","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":74,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke; McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Weighting; Ensemble forecasting; Probabilistic forecasting; Computer science; Probabilistic logic; Ensemble learning; Wavelet; Data mining; Artificial intelligence; Machine learning","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":[0.001712973,0.000464127,0.0005630565,0.000540985,0.0004850254,0.0007169142,0.0008127135,0.0006685676,0.0004502143],"category_scores_gemma":[0.002498134,0.0002436383,0.0005779761,0.0009343116,0.0002558771,0.0007582877,0.0005026732,0.0007824337,0.0000730855],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008807476,"about_ca_system_score_gemma":0.001000696,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04832842,"about_ca_topic_score_gemma":0.03450095,"domain_scores_codex":[0.9996634,0.0001353494,0.00002042517,0.00005125836,0.00009510031,0.0000345706],"domain_scores_gemma":[0.999099,0.000456296,0.00005589363,0.00008799073,0.0002646115,0.00003617385],"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":[0.00002049183,0.0000219503,0.001459355,0.000007769839,0.00002225753,0.00006040602,0.00002616705,0.9844068,0.0004144787,0.001781736,0.0002658912,0.01151266],"study_design_scores_gemma":[9.667908e-7,0.000003410511,0.0001301272,6.998767e-7,0.000002118433,0.000002044375,0.000003811939,0.9993914,0.0001149429,0.0002869453,0.00006170049,0.000001759525],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.4692216,0.0002832373,0.526099,0.0005890256,0.000047097,0.00006616914,0.0004739123,0.0003805528,0.00283946],"genre_scores_gemma":[0.9579246,0.00008814568,0.04122438,0.00002079158,0.00001303128,0.0000309807,0.0002457993,0.0000168084,0.0004355672],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.04832842,"threshold_uncertainty_score":0.09609425,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2673468259155963,"score_gpt":0.3827477327181475,"score_spread":0.1154009068025512,"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."}}