{"id":"W2071478111","doi":"10.1016/j.renene.2012.05.006","title":"A method for optimizing the location of wind farms","year":2012,"lang":"en","type":"article","venue":"Renewable Energy","topic":"Wind Energy Research and Development","field":"Engineering","cited_by":41,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Smoothing; Electricity; Wind power; Maxima and minima; Resource (disambiguation); Sensitivity (control systems); Population; Computer science; Wind speed; Mathematical optimization; Environmental science; Engineering; Meteorology; Geography; Mathematics; Electrical 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":[0.0003174338,0.00007929207,0.0001046379,0.00005289863,0.0000515214,0.00001130916,0.0001137815,0.00004509481,0.00001944611],"category_scores_gemma":[0.00002739085,0.00005664371,0.00003349562,0.0002334576,0.00001278148,0.00009126002,0.00002301459,0.00003031709,0.000001498984],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004234124,"about_ca_system_score_gemma":0.00003593707,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005986922,"about_ca_topic_score_gemma":0.00005830332,"domain_scores_codex":[0.9993446,0.00002239421,0.000136685,0.00006615782,0.0001224779,0.0003076993],"domain_scores_gemma":[0.9995848,0.000108903,0.00001955833,0.0001492098,0.0000627158,0.00007484572],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000006744749,0.00001097124,0.00004620793,0.00003913788,0.00004124899,7.217004e-8,0.0001339099,0.9796903,0.008406265,0.001062485,0.00227204,0.00829062],"study_design_scores_gemma":[0.0003820786,0.00003338301,0.0003019708,0.00004209638,0.00001616738,0.000005765151,0.0002324998,0.2266239,0.511988,0.0006469762,0.259527,0.0002000743],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001874706,0.002079373,0.9865943,0.00004998627,0.0001856007,0.00005562663,0.000001664838,0.00005587186,0.009102823],"genre_scores_gemma":[0.8864083,0.0001655735,0.1106893,0.00005208774,0.0002885895,0.00007633724,0.0000174894,0.00003289986,0.002269482],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8845336,"threshold_uncertainty_score":0.2309863,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01788709480314254,"score_gpt":0.2567107611086865,"score_spread":0.2388236663055439,"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."}}