{"id":"W1691090753","doi":"10.3968/j.est.1923847920120302.178","title":"Deriving to an Optimum Policy for Designing the Operating Parameters of Mahshahr Gas Turbine Power Plant Using a Self Learning Pareto Strategy","year":2012,"lang":"en","type":"article","venue":"Energy science and technology","topic":"Thermodynamic and Exergetic Analyses of Power and Cooling Systems","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Maximization; Pareto principle; Mathematical optimization; Computer science; Evolutionary algorithm; Multi-objective optimization; Power station; Power (physics); Engineering; Artificial intelligence; Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005163772,0.0001308449,0.0001863879,0.0003226383,0.0003193454,0.00005066621,0.0002870185,0.00008106059,0.000002033494],"category_scores_gemma":[0.0000943376,0.00009826561,0.00002410935,0.000575754,0.0001743949,0.0002156323,0.00006318177,0.0001023311,3.588625e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005217562,"about_ca_system_score_gemma":0.00006827317,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002409239,"about_ca_topic_score_gemma":0.00001784701,"domain_scores_codex":[0.9989859,0.00001772465,0.0001987496,0.0001787469,0.0001309444,0.0004879232],"domain_scores_gemma":[0.9995677,0.00004463379,0.00004535287,0.0001749525,0.0000700356,0.00009730475],"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":[0.00001080463,0.00003853642,0.002348052,0.00003362667,0.00008574726,0.000002292415,0.003984548,0.4685438,0.4565167,0.05511563,0.00001944984,0.01330075],"study_design_scores_gemma":[0.0001878041,0.0002952787,0.0001780787,0.00008129004,0.00003484615,0.00008575262,0.008454734,0.9608706,0.02842539,0.0004533789,0.0005937223,0.0003390909],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8559523,0.0003506709,0.1429484,0.00005527093,0.0001114303,0.00007765729,0.000001865751,0.0001526282,0.0003498029],"genre_scores_gemma":[0.9894647,0.00001552722,0.01038885,0.00004014065,0.00004392776,0.00001776334,0.000001053601,0.00001633257,0.00001172985],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4923268,"threshold_uncertainty_score":0.4007155,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01231542623912778,"score_gpt":0.2446944480749104,"score_spread":0.2323790218357826,"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."}}