{"id":"W2599410335","doi":"10.1061/(asce)ww.1943-5460.0000397","title":"Hydrodynamic Modeling of the St. Lawrence Fluvial Estuary. I: Model Setup, Calibration, and Validation","year":2017,"lang":"en","type":"article","venue":"Journal of Waterway Port Coastal and Ocean Engineering","topic":"Oceanographic and Atmospheric Processes","field":"Earth and Planetary Sciences","cited_by":50,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique; Environment and Climate Change Canada","funders":"","keywords":"Bathymetry; Calibration; Geology; Interpolation (computer graphics); Fluvial; Tidal Model; Discretization; Beach morphodynamics; Terrain; Geodesy; Tidal river; Estuary; Hydrology (agriculture); Geomorphology; Geotechnical engineering; Sediment transport; Mathematics; Sediment; Physics; Geography; Statistics; Oceanography","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001964896,0.00009213237,0.000144998,0.00002008319,0.0001782089,0.00008094261,0.0001640356,0.00003217705,0.000007174637],"category_scores_gemma":[0.00002741516,0.00005714143,0.00003891488,0.00003805153,0.00005945473,0.0006133713,0.00003171767,0.0001102521,5.942978e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":9.778591e-7,"about_ca_system_score_gemma":0.00003423875,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007679041,"about_ca_topic_score_gemma":0.00003599361,"domain_scores_codex":[0.9993677,0.000006427334,0.000258449,0.00008296658,0.000172178,0.0001122877],"domain_scores_gemma":[0.9995724,0.00001252544,0.0002029194,0.00009129292,0.00005208782,0.00006876241],"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":[0.00001481239,0.00000544431,0.1248301,0.00005206562,0.00001686804,0.000004154993,0.0002283645,0.8735983,0.0002349558,0.00001425689,0.000009897412,0.0009907935],"study_design_scores_gemma":[0.0001539505,0.00004726282,0.015043,0.00006841347,0.00002174165,0.00005397033,0.00005947894,0.9835835,0.0003939683,0.0004886285,0.00001196229,0.00007420107],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9809106,0.0001795026,0.01855411,0.0001025547,0.0001373289,0.00003336295,0.00002322879,0.00000567169,0.00005363334],"genre_scores_gemma":[0.998436,0.0001832444,0.001265593,0.000009137229,0.00006390116,2.859662e-8,0.000005297659,0.000003306111,0.00003350604],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1099852,"threshold_uncertainty_score":0.233016,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008209956709159989,"score_gpt":0.1804464571184728,"score_spread":0.1722365004093128,"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."}}