{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001358156,0.0006491161,0.0005302727,0.002253362,0.0002275329,0.001109743,0.000242558,0.0003278802,0.001133887],"category_scores_gemma":[0.004318405,0.0001364378,0.0004279312,0.001642666,0.0003274547,0.0008010108,0.0005685759,0.0002273562,0.0001048973],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005176498,"about_ca_system_score_gemma":0.0002268687,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003848751,"about_ca_topic_score_gemma":0.005473291,"domain_scores_codex":[0.9994165,0.0003670077,0.00003500935,0.00004569185,0.0001039587,0.00003181964],"domain_scores_gemma":[0.996799,0.002059679,0.0002937232,0.0001675305,0.0004391167,0.0002409403],"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":[0.0005525522,0.0001457761,0.05715199,0.0001153244,0.0001624596,0.0002870156,0.00009059011,0.9049342,0.002674881,0.002290256,0.001038953,0.03055603],"study_design_scores_gemma":[0.00001167724,0.0001430364,0.01071282,0.000008347923,0.00001669816,0.00002890643,0.00009228451,0.9869763,0.0008429906,0.0009225279,0.0002366188,0.000007768329],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9655901,0.00008046114,0.03080966,0.00007617247,0.00001867876,0.00006745983,0.0003888096,0.0001960126,0.002772663],"genre_scores_gemma":[0.9930037,0.00001950739,0.006616889,0.000003725829,0.000003617701,0.00001149715,0.0001882489,0.00001031519,0.0001424329],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003848751,"threshold_uncertainty_score":0.0076527,"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."}}