{"id":"W4311789551","doi":"10.3390/en15249484","title":"Deep Learning for Modeling an Offshore Hybrid Wind–Wave Energy System","year":2022,"lang":"en","type":"article","venue":"Energies","topic":"Wind Energy Research and Development","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Nemzeti Kutatási, Fejlesztési és Innovaciós Alap; Horizon 2020 Framework Programme; European Commission","keywords":"Wind power; Offshore wind power; Installation; Turbine; Marine engineering; Renewable energy; Power (physics); Converters; Energy (signal processing); Computer science; Engineering; Electrical engineering; Mechanical engineering; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003909341,0.0006749919,0.0004640335,0.0003745402,0.0002553894,0.0006510827,0.0006234795,0.0009888093,0.001909302],"category_scores_gemma":[0.0006402023,0.0003584719,0.0005763082,0.0003366556,0.0003458758,0.000473332,0.0004859049,0.0009701299,0.0002469966],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006951099,"about_ca_system_score_gemma":0.0007619014,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01753428,"about_ca_topic_score_gemma":0.01323665,"domain_scores_codex":[0.9998894,0.00002332361,0.00000703939,0.00002725834,0.00002512157,0.00002785644],"domain_scores_gemma":[0.9998192,0.00009215957,0.00002317772,0.000007233733,0.00004826019,0.000009989283],"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":[0.00001606877,0.00001661028,0.000686059,0.00001700796,0.000009587235,0.00003067605,0.000007767497,0.9926273,0.0003672507,0.0005576418,0.0001831149,0.00548095],"study_design_scores_gemma":[8.335815e-7,0.000003656747,0.0000733561,0.000001560617,0.000001079395,0.0000014632,0.000001664533,0.9996458,0.00005896942,0.000160941,0.00004972779,7.874381e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2785568,0.001278621,0.7026963,0.0007243846,0.000138306,0.0001245698,0.0008707679,0.001006253,0.014604],"genre_scores_gemma":[0.9742459,0.0002273916,0.02011037,0.0000806269,0.00001962152,0.000138794,0.000347766,0.00001922774,0.004810207],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01753428,"threshold_uncertainty_score":0.03486443,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0168133661242377,"score_gpt":0.2081107804797162,"score_spread":0.1912974143554785,"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."}}