{"id":"W4385502792","doi":"10.1016/j.est.2023.108496","title":"Integrating wind energy and compressed air energy storage for remote communities: A bi-level programming approach","year":2023,"lang":"en","type":"article","venue":"Journal of Energy Storage","topic":"Microgrid Control and Optimization","field":"Engineering","cited_by":47,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"","keywords":"Compressed air energy storage; Energy storage; Compressed air; Wind power; Energy (signal processing); Automotive engineering; Recuperator; Gas compressor; Computer data storage; Process engineering; Systems design; Engineering; Computer science; Simulation; Mechanical engineering; Electrical engineering; Power (physics); Systems 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003917753,0.0002898838,0.0004910256,0.000450348,0.0002078805,0.0001164998,0.0003017099,0.0001587444,0.000005818605],"category_scores_gemma":[0.00003846017,0.0002635129,0.0001729482,0.0003992067,0.00005808487,0.0002991057,0.00006811704,0.0002174818,1.765819e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009982589,"about_ca_system_score_gemma":0.00004686581,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003542718,"about_ca_topic_score_gemma":0.0002342314,"domain_scores_codex":[0.998525,0.0001008251,0.0006011362,0.000141643,0.0002385973,0.0003927799],"domain_scores_gemma":[0.998893,0.000228751,0.0002797,0.0002260217,0.0002183693,0.0001541174],"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":[0.00006390391,0.00004394513,0.000009889188,0.00009514828,0.0002354506,0.00002946783,0.0008210044,0.8914281,0.002342181,0.001598517,0.001870994,0.1014614],"study_design_scores_gemma":[0.001176443,0.0001504175,0.00004245727,0.0001411241,0.00005458607,0.00007076043,0.001012454,0.882251,0.0004468678,0.0002158471,0.1141623,0.0002758492],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01358563,0.003495685,0.9815159,0.00007716969,0.0005148282,0.0000711601,0.00003006322,0.0001948239,0.0005147823],"genre_scores_gemma":[0.942672,0.001559726,0.05412846,0.0001706411,0.0006883586,0.00001527372,0.0001277744,0.0001210174,0.0005167496],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9290864,"threshold_uncertainty_score":0.9999817,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02074805752304261,"score_gpt":0.216441742285865,"score_spread":0.1956936847628224,"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."}}