{"id":"W2566529875","doi":"10.1002/2016wr019752","title":"A platform for probabilistic Multimodel and Multiproduct Streamflow Forecasting","year":2016,"lang":"en","type":"article","venue":"Water Resources Research","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":40,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Oceanic and Atmospheric Administration; Manitoba Medical Service Foundation; National Aeronautics and Space Administration","keywords":"Streamflow; Flood forecasting; Probabilistic logic; Probabilistic forecasting; Computer science; Environmental science; Consensus forecast; Calibration; Hydrological modelling; Meteorology; Econometrics; Climatology; Drainage basin; Statistics; Mathematics; Geography; Geology; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001304415,0.0001233512,0.0001318762,0.00007801021,0.0005437427,0.00003708634,0.0002201057,0.00005519282,0.0002007928],"category_scores_gemma":[0.0002469396,0.00006027325,0.00003054722,0.00007220749,0.0006989095,0.0001773003,0.0007643795,0.000100221,0.0001824887],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007116207,"about_ca_system_score_gemma":0.000001440847,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001794853,"about_ca_topic_score_gemma":0.0001276404,"domain_scores_codex":[0.9982719,0.00006557957,0.0001537837,0.0004961951,0.0002903435,0.0007221483],"domain_scores_gemma":[0.9993852,0.0002589107,0.00001690489,0.0002313605,0.00002040209,0.00008721808],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.002159625,0.0005689142,0.2760457,0.000607422,0.0003078688,0.00007784284,0.04741616,0.004087275,0.09341039,0.0003469068,0.01291628,0.5620556],"study_design_scores_gemma":[0.0104581,0.002548516,0.02449746,0.0003201662,0.00009828214,0.00004518828,0.001810325,0.1959941,0.061231,0.06710524,0.6341028,0.001788807],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9939461,0.00002064025,0.0004623529,0.002188507,0.00002570497,0.0007657319,0.000007785579,0.00003656336,0.002546556],"genre_scores_gemma":[0.991833,0.00001523389,0.00132594,0.00003362896,0.00006065113,0.000239674,0.000002941564,0.00001666817,0.006472229],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6211866,"threshold_uncertainty_score":0.4182085,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0929445231133932,"score_gpt":0.3040655127789008,"score_spread":0.2111209896655076,"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."}}