{"id":"W4383100035","doi":"10.1017/eds.2023.11","title":"A novel workflow for streamflow prediction in the presence of missing gauge observations","year":2023,"lang":"en","type":"article","venue":"Environmental Data Science","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"DeepMind","keywords":"Streamflow; Missing data; Imputation (statistics); Categorical variable; Flood forecasting; Computer science; Warning system; Leverage (statistics); Flood myth; Regression; Econometrics; Environmental science; Data mining; Statistics; Artificial intelligence; Machine learning; Drainage basin; Cartography; Mathematics; Geography","routes":{"ca_aff":true,"ca_fund":false,"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":[0.002052834,0.0008154557,0.0006599196,0.0008234917,0.0008097953,0.001357696,0.001550486,0.0006368843,0.002721016],"category_scores_gemma":[0.004347304,0.0003979313,0.0008894386,0.0007537988,0.0004197687,0.001240514,0.001365635,0.0009303351,0.001366567],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006291086,"about_ca_system_score_gemma":0.002777905,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01266082,"about_ca_topic_score_gemma":0.009095444,"domain_scores_codex":[0.9992729,0.00007803658,0.00008670032,0.0002996887,0.0002039755,0.00005875486],"domain_scores_gemma":[0.9981755,0.0005694843,0.0001544407,0.0005282944,0.0004372283,0.0001350863],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001039412,0.0004997123,0.03128746,0.0002433324,0.0002446919,0.001579266,0.0008795775,0.3264665,0.03264094,0.01272853,0.01885139,0.5735392],"study_design_scores_gemma":[0.00002335053,0.00001981315,0.0009521954,0.000008418507,0.00001071836,0.00004872808,0.00003506353,0.9847778,0.007226102,0.003904652,0.002978603,0.00001461861],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02621832,0.00004469455,0.93971,0.0001537121,0.00007673273,0.0001781848,0.001529181,0.03107354,0.001015606],"genre_scores_gemma":[0.3748678,0.0001346289,0.6163304,0.0001262119,0.00005592251,0.0002455256,0.005026381,0.0007679954,0.002445113],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01266082,"threshold_uncertainty_score":0.02517426,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06838411252735653,"score_gpt":0.2704303668683858,"score_spread":0.2020462543410292,"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."}}