{"id":"W4386959852","doi":"10.9753/icce.v37.papers.38","title":"FLEXIBLE FLUID-STRUCTURE INTERACTION OF A FLEXIBLE PLANT MODEL FOR NATURE-BASED SOLUTIONS","year":2023,"lang":"en","type":"article","venue":"Coastal Engineering Proceedings","topic":"Coastal and Marine Dynamics","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique; Wilfrid Laurier University; University of Ottawa","funders":"","keywords":"Drag; Pace; Fluid–structure interaction; Computer science; Marsh; Marine engineering; Variety (cybernetics); Field (mathematics); Accretion (finance); Software; Salt marsh; Environmental science; Geology; Aerospace engineering; Engineering; Oceanography; Finite element method; Physics; Ecology; Mathematics; Geodesy; Structural engineering","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":[0.0001075473,0.0001537803,0.0001602525,0.0002403386,0.00008828372,0.00003993029,0.0001460444,0.0001132982,0.00003876654],"category_scores_gemma":[0.00007164147,0.0001449832,0.00008327749,0.0004443432,0.00002393139,0.0002241693,0.00003877443,0.0002006467,0.000007019322],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007071873,"about_ca_system_score_gemma":0.00004908532,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001224891,"about_ca_topic_score_gemma":0.0001654856,"domain_scores_codex":[0.9990863,8.69704e-7,0.0001868597,0.0002150354,0.0001619633,0.0003490192],"domain_scores_gemma":[0.9996348,0.00005605192,0.00005667211,0.00006690235,0.00009758569,0.00008797856],"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":[0.0001433028,0.00001360813,0.001636106,0.0003991639,0.00002666945,0.000001206201,0.0002039367,0.9787996,0.007083057,0.004397692,0.001847028,0.00544856],"study_design_scores_gemma":[0.0002506401,0.00008643803,0.003513847,0.00005121199,0.00001568199,0.000008160532,0.00007186842,0.9909817,0.002578866,0.0005423843,0.001735106,0.0001640835],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8910265,0.00009972186,0.09945208,0.0003549074,0.001561884,0.0007242167,0.003783642,0.001161222,0.001835852],"genre_scores_gemma":[0.9952387,0.000005830016,0.002882831,0.00002778072,0.00008056708,0.000008506741,0.0007467623,0.00001149382,0.0009974919],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1042123,"threshold_uncertainty_score":0.5912243,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01720294820697588,"score_gpt":0.2179314748893189,"score_spread":0.200728526682343,"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."}}