{"id":"W3018718829","doi":"10.1109/tste.2020.2986586","title":"Operations &amp; Maintenance Optimization of Wind Turbines Integrating Wind and Aging Information","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Sustainable Energy","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":147,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Wind power; Offshore wind power; Turbine; Reliability engineering; Revenue; Optimal maintenance; Production (economics); Maintenance engineering; Reliability (semiconductor); Renewable energy; Engineering; Computer science; Marine engineering; Power (physics); Business","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":[0.001075706,0.0007329335,0.0007471606,0.0003794184,0.0003057536,0.0007564955,0.0007074852,0.0006080478,0.001162826],"category_scores_gemma":[0.002377879,0.0003239827,0.0003635882,0.0005769842,0.0003442283,0.0009958275,0.000613198,0.0005581127,0.000111585],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007741182,"about_ca_system_score_gemma":0.00122114,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004516933,"about_ca_topic_score_gemma":0.00413118,"domain_scores_codex":[0.9995373,0.0001921192,0.00001911711,0.00007980709,0.00007466416,0.0000969844],"domain_scores_gemma":[0.9990606,0.0005343606,0.0001942952,0.00005996383,0.00007085912,0.00008001879],"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":[0.00006024939,0.00005191762,0.0008436948,0.00002559156,0.00001278762,0.00004949141,0.00001414762,0.9794956,0.0008979068,0.002985336,0.0003266654,0.0152365],"study_design_scores_gemma":[0.000004468727,0.00003487636,0.0002703166,0.000002106596,0.000004424696,0.00001213152,0.000009805689,0.9980785,0.0002148474,0.001245277,0.0001214608,0.000001854612],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3965967,0.0006571196,0.5926428,0.0004883101,0.00004761478,0.0001691645,0.0003295961,0.0002011129,0.008867575],"genre_scores_gemma":[0.9829161,0.0001550561,0.01586992,0.00001500269,0.000008624507,0.00002947159,0.00007229467,0.00001670994,0.0009168767],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004516933,"threshold_uncertainty_score":0.008981287,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005592530214258211,"score_gpt":0.1885301410360039,"score_spread":0.1829376108217457,"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."}}