{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002151876,0.0001675003,0.0001873177,0.0001570334,0.0004333927,0.00006249451,0.0001923914,0.00002897795,0.00005275942],"category_scores_gemma":[0.00001313156,0.000178467,0.00007106709,0.0001329229,0.00001212232,0.0001455932,0.0001201574,0.0001776661,0.000002414202],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002330065,"about_ca_system_score_gemma":0.00004106292,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007208245,"about_ca_topic_score_gemma":0.0000236713,"domain_scores_codex":[0.9987309,0.00005450642,0.0002076806,0.0002294365,0.0003134029,0.0004640971],"domain_scores_gemma":[0.9995851,0.00004013393,0.00001848395,0.0001856715,0.00004253982,0.000128061],"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":[0.00002175083,0.000009430702,0.00001698462,0.00004742711,0.00004742244,0.00002412216,0.0002891365,0.9860724,0.0005592036,0.00370171,0.0004326061,0.008777823],"study_design_scores_gemma":[0.0002316941,0.0000768168,0.00001197625,0.00001017329,0.000005782592,0.00002890669,0.002459351,0.9647126,0.002922596,0.0001406881,0.02917061,0.0002288704],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8604577,0.002116185,0.1277803,0.00003483017,0.0008579395,0.000127,0.00001318233,0.001388312,0.007224581],"genre_scores_gemma":[0.9965822,0.00004584943,0.001837678,0.00002025993,0.0002277821,0.000232004,0.0001621611,0.0000681681,0.0008239326],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1361245,"threshold_uncertainty_score":0.7277675,"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."}}