{"id":"W4321178856","doi":"10.1002/we.2809","title":"Adding wind power to a wind‐rich grid: Evaluating secondary suitability metrics","year":2023,"lang":"en","type":"article","venue":"Wind Energy","topic":"Integrated Energy Systems Optimization","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"Fisheries and Oceans Canada; National Oceanic and Atmospheric Administration; Nova Scotia Department of Energy and Mines; Atlantic Canada Opportunities Agency; Government of Canada; Iowa State University","keywords":"Wind power; Electricity; Dispatchable generation; Renewable energy; Geospatial analysis; Grid; Environmental economics; Electric power system; Computer science; Environmental science; Engineering; Power (physics); Distributed generation; Economics; Electrical engineering; Geography; Remote sensing","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008808864,0.0003486006,0.0003698815,0.0007867478,0.000161853,0.0001102353,0.0003070221,0.000270245,0.0004746596],"category_scores_gemma":[0.0004676639,0.0003644504,0.0001030631,0.003367365,0.00002134829,0.0003036887,0.0001081668,0.0002873718,0.0002106411],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004203012,"about_ca_system_score_gemma":0.00009125817,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003668701,"about_ca_topic_score_gemma":0.00009894952,"domain_scores_codex":[0.9976164,0.0001208862,0.0005654424,0.0004731613,0.0005210145,0.0007030946],"domain_scores_gemma":[0.9987475,0.0002214467,0.00006382834,0.0005285668,0.0002270789,0.0002115621],"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":[0.000008647257,0.00001258966,0.0001901553,0.00003620244,0.00008083004,0.00001344677,0.0008639195,0.9822683,0.003338525,0.0005308528,0.007881138,0.004775413],"study_design_scores_gemma":[0.001086899,0.0002803657,0.006156472,0.0002483647,0.00008321139,0.00003622319,0.002141411,0.8734258,0.01316571,0.0002694284,0.1014714,0.001634731],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9316959,0.0003339124,0.005448997,0.0001259987,0.00420867,0.0002511467,0.00009027341,0.002010105,0.05583499],"genre_scores_gemma":[0.9957663,0.00001555362,0.001809983,0.0001628348,0.0004530907,0.00002296706,0.0001999251,0.0001401183,0.001429241],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1088425,"threshold_uncertainty_score":0.9998807,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01718010195765572,"score_gpt":0.2498417825994152,"score_spread":0.2326616806417595,"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."}}