{"id":"W3204394459","doi":"10.21203/rs.3.rs-934134/v1","title":"Optimization of a Heliostat Field by Multiobjective Particle Swarm Optimization (MOPSO) Algorithm Based on Energy, Exergy, and Economic Point of Views","year":2021,"lang":"en","type":"preprint","venue":"Research Square","topic":"Solar Thermal and Photovoltaic Systems","field":"Energy","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"La Cité Collégiale","funders":"","keywords":"Heliostat; Exergy; Payback period; Exergy efficiency; Particle swarm optimization; Environmental science; Field (mathematics); Computer science; Process engineering; Solar energy; Engineering; Mathematics; Mathematical optimization; Electrical engineering; Economics","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":[0.0009067273,0.0002625003,0.0005714131,0.0002487952,0.0000956795,0.00007930699,0.000231729,0.0003597151,0.0006402879],"category_scores_gemma":[0.0003034815,0.0002566317,0.0001664393,0.000260416,0.0001037467,0.0001010372,0.0003180934,0.0003916383,0.000003075702],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002278488,"about_ca_system_score_gemma":0.0002676486,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02746015,"about_ca_topic_score_gemma":0.0005590047,"domain_scores_codex":[0.9969174,0.0009800487,0.000628337,0.0005995437,0.0005171447,0.0003575522],"domain_scores_gemma":[0.99765,0.000699823,0.0003619326,0.0006248957,0.0005125605,0.0001508396],"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":[0.0002273503,0.0002253588,0.0002237378,0.0004844597,0.00008387859,0.000005798448,0.000752055,0.9869385,0.001612144,0.00008510312,0.0003212035,0.009040372],"study_design_scores_gemma":[0.0006991987,0.0003727969,0.00004578831,0.0007714014,0.00001644939,0.000001476317,0.000887293,0.8314231,0.1652468,0.00006118079,0.0002800855,0.0001943707],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2193607,0.008490908,0.7554159,0.0007563172,0.001228276,0.003651512,0.001749862,0.0002101577,0.00913631],"genre_scores_gemma":[0.9910572,0.001054445,0.006320115,0.00005515166,0.0001168616,0.0003161074,0.0007458833,0.00006587841,0.0002683156],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7716965,"threshold_uncertainty_score":0.9999886,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03227592562136492,"score_gpt":0.318431947777614,"score_spread":0.286156022156249,"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."}}