{"id":"W4386134088","doi":"10.3390/hydrology10090177","title":"Modeling Hydrodynamic Behavior of the Ottawa River: Harnessing the Power of Numerical Simulation and Machine Learning for Enhanced Predictability","year":2023,"lang":"en","type":"article","venue":"Hydrology","topic":"Flood Risk Assessment and Management","field":"Environmental Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa; Université Laval","funders":"","keywords":"Predictability; Flood myth; Watershed; Hydrology (agriculture); Climate change; Streamflow; Environmental science; Flow (mathematics); Computer science; Process (computing); Drainage basin; Machine learning; Geology; Geography; Statistics; Geotechnical engineering; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003723596,0.00006609382,0.0001146695,0.00001821948,0.0001324,0.000003618217,0.0001296699,0.00003857044,0.00004989234],"category_scores_gemma":[0.00005291623,0.00004081584,0.00004608866,0.0001352434,0.0001934905,0.00005639147,0.0002151012,0.00008310359,0.00000232575],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002061382,"about_ca_system_score_gemma":0.000003565399,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002721871,"about_ca_topic_score_gemma":0.0001295226,"domain_scores_codex":[0.9992954,0.00008776892,0.0001859085,0.000169182,0.0001247544,0.0001369576],"domain_scores_gemma":[0.9996154,0.0001189298,0.00009029962,0.0001551534,0.000006025674,0.00001417173],"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":[0.00001267353,0.000027621,0.08251055,0.000007693407,0.000006407407,8.676868e-8,0.0005072001,0.9100474,0.005475418,0.00002035787,0.000004182909,0.001380433],"study_design_scores_gemma":[0.000200797,0.00006768221,0.1005974,0.000002729357,0.00003536763,2.083484e-7,0.00003860907,0.8983429,0.0002621789,0.0003439517,0.00007238675,0.00003582054],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9597289,0.00001292022,0.03948456,0.000181158,0.00007296337,0.0003630233,0.000002827681,0.00002000533,0.0001336355],"genre_scores_gemma":[0.9996358,0.000005564067,0.0002131808,0.00002049796,0.000005507163,0.00003213609,0.000005643817,0.000006858786,0.00007482924],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03990687,"threshold_uncertainty_score":0.1664422,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01264760600106161,"score_gpt":0.2645232795877494,"score_spread":0.2518756735866878,"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."}}