{"id":"W2617986634","doi":"10.1061/(asce)hy.1943-7900.0001324","title":"Meandering Morphodynamics: Insights from Laboratory and Numerical Experiments and Beyond","year":2017,"lang":"en","type":"article","venue":"Journal of Hydraulic Engineering","topic":"Hydrology and Sediment Transport Processes","field":"Environmental Science","cited_by":52,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Technische Universität München","keywords":"Beach morphodynamics; Sinuosity; Geology; Bank erosion; Flow (mathematics); Curvature; Channel (broadcasting); Geotechnical engineering; Meander (mathematics); Open-channel flow; STREAMS; Erosion; Geometry; Fluvial; Geomorphology; Kinematics; Hydraulics; Bed load; Hydrology (agriculture); Sediment transport; Sediment; Engineering; Mathematics; Structural basin; Physics; Computer science","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004192803,0.0002906969,0.0005219531,0.0001897142,0.0002693463,0.0005790204,0.0005311473,0.0005393089,0.0007004627],"category_scores_gemma":[0.001066852,0.0001623805,0.0002179515,0.000183948,0.001225193,0.001106283,0.0004832305,0.0007152904,0.000101732],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002801415,"about_ca_system_score_gemma":0.0001606998,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007255127,"about_ca_topic_score_gemma":0.0006524523,"domain_scores_codex":[0.9997819,0.00006009544,0.00001646274,0.00005555084,0.00005193046,0.0000340179],"domain_scores_gemma":[0.9993563,0.0004102092,0.00006937565,0.0000979328,0.00003295093,0.00003322159],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004949695,0.001413313,0.01208284,0.0005037231,0.0000477155,0.0004531114,0.001064031,0.07123217,0.8359355,0.009036496,0.001195117,0.066541],"study_design_scores_gemma":[0.0002223618,0.004906369,0.03437287,0.0001431397,0.000104275,0.0005991403,0.0008613594,0.591688,0.3347218,0.02450708,0.00765697,0.0002166617],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9647714,0.001402005,0.02921294,0.0003847988,0.00005833814,0.00004908316,0.0001878699,0.0002592904,0.003674372],"genre_scores_gemma":[0.9918392,0.0008242938,0.006850265,0.00004707931,0.00002122494,0.00003674679,0.00006498898,0.00002163719,0.0002944307],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0007255127,"threshold_uncertainty_score":0.002343297,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005817644956993768,"score_gpt":0.2088608274521604,"score_spread":0.2030431824951666,"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."}}