{"id":"W4388281732","doi":"10.1109/cpese59653.2023.10303240","title":"Emission Mitigation Dispatch in Wind Power Generation with Expedited Machine Learning","year":2023,"lang":"en","type":"article","venue":"","topic":"Electric Power System Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Wind power; Computer science; Scheme (mathematics); Artificial neural network; Smart grid; Grid; Feed forward; Implementation; Feedforward neural network; Artificial intelligence; Embedded system; Control engineering; Engineering; Electrical engineering; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005453549,0.0006432039,0.000628015,0.0002860596,0.0002231724,0.0004704198,0.0005698932,0.0004789725,0.001930939],"category_scores_gemma":[0.0009668938,0.0002271618,0.0002455916,0.0002409292,0.0002878565,0.0005550869,0.000527948,0.0006199394,0.0002732831],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004429203,"about_ca_system_score_gemma":0.0006764647,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003091185,"about_ca_topic_score_gemma":0.003778058,"domain_scores_codex":[0.9997976,0.00007815325,0.000009068093,0.00003283082,0.00005374868,0.00002853368],"domain_scores_gemma":[0.9997415,0.0001299913,0.00003685131,0.00002308107,0.00005401384,0.00001465341],"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":[0.00005698005,0.00003413911,0.0002609925,0.00003648967,0.00001306606,0.00003202497,0.0000102427,0.9638279,0.000950951,0.001402738,0.0002473921,0.03312712],"study_design_scores_gemma":[0.000004737621,0.00002221701,0.00006757574,0.000001947056,0.000001796247,0.000004211457,0.000001578169,0.9989182,0.0003635566,0.0004002666,0.0002124438,0.000001478805],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06978508,0.0003132425,0.9206758,0.0002098687,0.00006680673,0.00009132329,0.00005683456,0.0009635416,0.007837506],"genre_scores_gemma":[0.9237064,0.00007989712,0.07368752,0.00005856837,0.00002333315,0.00006570916,0.00006139048,0.00004511786,0.002272134],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003091185,"threshold_uncertainty_score":0.006459653,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006088527454845526,"score_gpt":0.1936495833463589,"score_spread":0.1875610558915134,"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."}}