{"id":"W2065168131","doi":"10.1016/j.jhydrol.2013.08.028","title":"Bias-corrected short-range Member-to-Member ensemble forecasts of reservoir inflow","year":2013,"lang":"en","type":"article","venue":"Journal of Hydrology","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":23,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"BC Hydro","keywords":"Ensemble forecasting; Range (aeronautics); Downscaling; Computer science; Sampling (signal processing); Ensemble learning; Ensemble average; Statistics; Sample (material); Environmental science; Meteorology; Mathematics; Artificial intelligence; Climatology; Geology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.001522501,0.0004146951,0.00051092,0.0003411669,0.0003319292,0.0005639701,0.0005637866,0.0007394249,0.0005900186],"category_scores_gemma":[0.004039798,0.0002971284,0.0005006405,0.0004125556,0.000176743,0.001064495,0.0004658117,0.000840157,0.0002662303],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000460123,"about_ca_system_score_gemma":0.0009007341,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0133935,"about_ca_topic_score_gemma":0.01654165,"domain_scores_codex":[0.9997696,0.00006436874,0.00001792683,0.00005197916,0.0000578343,0.00003830142],"domain_scores_gemma":[0.9983196,0.0005084464,0.0001384011,0.0002526016,0.0006780199,0.0001029631],"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":[0.0003592762,0.000103908,0.02551336,0.00001690408,0.000164112,0.00003760051,0.00005112099,0.9254999,0.004048061,0.0005683594,0.001965214,0.04167226],"study_design_scores_gemma":[0.00001133707,0.00001503781,0.004343806,0.000001769817,0.00001344113,0.000004375596,0.000006563538,0.9945146,0.0007591503,0.0002020175,0.0001213409,0.000006573053],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9514027,0.0002110853,0.0450099,0.0001786914,0.0002063641,0.00001564985,0.0007585698,0.0003841454,0.001832755],"genre_scores_gemma":[0.9895111,0.00005786984,0.008990742,0.0000208429,0.00004195911,0.000008665747,0.0008518195,0.00002820929,0.0004886974],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0133935,"threshold_uncertainty_score":0.02663106,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03256722661285246,"score_gpt":0.2564079265709787,"score_spread":0.2238406999581262,"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."}}