{"id":"W2169793408","doi":"10.1623/hysj.52.3.508","title":"Seasonal reservoir inflow forecasting with low-frequency climatic indices: a comparison of data-driven methods","year":2007,"lang":"en","type":"article","venue":"Hydrological Sciences Journal","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Perceptron; Inflow; Artificial neural network; El Niño Southern Oscillation; Environmental science; Climatology; Multilayer perceptron; Series (stratigraphy); Time series; Computer science; Meteorology; Machine learning; Geography; Geology","routes":{"ca_aff":true,"ca_fund":false,"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":[0.003567243,0.000560587,0.0005820065,0.000896577,0.0001633963,0.0007915968,0.0008601045,0.0006338318,0.0006734475],"category_scores_gemma":[0.00861829,0.0004011182,0.0005720673,0.0006810715,0.0001646878,0.001016626,0.0005188613,0.0005704669,0.0001743607],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000599779,"about_ca_system_score_gemma":0.0008337339,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006771967,"about_ca_topic_score_gemma":0.006570941,"domain_scores_codex":[0.9992316,0.0003853086,0.00006220163,0.00008758662,0.0002051888,0.00002812758],"domain_scores_gemma":[0.9961089,0.002647157,0.0002131805,0.0001883782,0.0007657593,0.00007660028],"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":[0.0005385973,0.0003598384,0.01788932,0.0002174079,0.0003662302,0.0000645384,0.00008327203,0.7394329,0.002365661,0.002413488,0.0008171278,0.2354516],"study_design_scores_gemma":[0.00001793998,0.00002794298,0.001066555,0.000007527108,0.00001015567,0.000005302137,0.000005857998,0.9976295,0.0006701369,0.0003790586,0.0001728938,0.00000719453],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3611584,0.001240377,0.6291158,0.0009041515,0.0002116668,0.0002008158,0.0006738855,0.001274868,0.00522007],"genre_scores_gemma":[0.903208,0.0003597566,0.09473936,0.0001126432,0.00005662046,0.0001202649,0.0006115669,0.00005007027,0.0007418253],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006771967,"threshold_uncertainty_score":0.01886564,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1560253736332065,"score_gpt":0.3975446721218076,"score_spread":0.241519298488601,"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."}}