{"id":"W4311711331","doi":"10.1016/j.jfluidstructs.2022.103812","title":"Predicting hydrodynamic forces on heave plates using a data-driven modelling architecture","year":2022,"lang":"en","type":"article","venue":"Journal of Fluids and Structures","topic":"Wave and Wind Energy Systems","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Victoria","funders":"Natural Resources Canada","keywords":"Nonlinear system; Drag; Work (physics); Fictitious force; Acceleration; Added mass; Inertial frame of reference; Block (permutation group theory); Hysteresis; Mechanics; Structural engineering; Engineering; Physics; Acoustics; Geometry; Classical mechanics; Mathematics; Mechanical engineering; Vibration","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002222673,0.000799669,0.0006058274,0.0004004919,0.0004953879,0.0009492926,0.00137562,0.001023608,0.002742742],"category_scores_gemma":[0.0007617075,0.0006742124,0.0006720085,0.0003100676,0.0005198102,0.0008462504,0.0008098257,0.0007977909,0.0005238201],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006231884,"about_ca_system_score_gemma":0.001200522,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01583611,"about_ca_topic_score_gemma":0.01406798,"domain_scores_codex":[0.9999039,0.0000124968,0.000007333371,0.00002289696,0.00003611623,0.00001719955],"domain_scores_gemma":[0.9996494,0.0001245067,0.00003276841,0.00005177943,0.0001028816,0.00003864444],"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":[0.00003823084,0.00004768372,0.0009108037,0.00001532265,0.0000112651,0.00002890831,0.00001979282,0.9890733,0.003084973,0.0004018925,0.0001413459,0.006226422],"study_design_scores_gemma":[0.000002444576,0.000005003342,0.00005444183,4.633046e-7,9.715325e-7,0.000001392644,0.000001924987,0.9994068,0.0004153385,0.00007369823,0.00003596845,0.000001564491],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.4356159,0.000111026,0.5518681,0.0002880454,0.0001051756,0.0001542171,0.000564651,0.004737374,0.006555513],"genre_scores_gemma":[0.9513366,0.00005429781,0.0460976,0.00003634441,0.00001322324,0.00008834443,0.000323794,0.000121983,0.001927814],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01583611,"threshold_uncertainty_score":0.03148788,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02204251463085695,"score_gpt":0.2269307591737823,"score_spread":0.2048882445429254,"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."}}