{"id":"W3167621926","doi":"10.3390/su13116448","title":"A Novel Electricity and Freshwater Production System: Performance Analysis from Reliability and Exergoeconomic Viewpoints with Multi-Objective Optimization","year":2021,"lang":"en","type":"article","venue":"Sustainability","topic":"Thermodynamic and Exergetic Analyses of Power and Cooling Systems","field":"Engineering","cited_by":52,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Process engineering; Cogeneration; Exergy; Particle swarm optimization; Electricity generation; Engineering; Syngas; Exergy efficiency; Reliability (semiconductor); Cost of electricity by source; Wood gas generator; Electricity; Environmental science; Waste management; Automotive engineering; Power (physics); Mathematical optimization; Mathematics; Electrical 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":[],"consensus_categories":[],"category_scores_codex":[0.0002962805,0.0001826969,0.0003786457,0.00008631359,0.0001225236,0.00004952628,0.00005238258,0.00008396029,0.00001365761],"category_scores_gemma":[0.00007925614,0.0001569328,0.00005775573,0.0003704125,0.00008412844,0.0002022039,0.00003215717,0.0001293983,6.736498e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005466676,"about_ca_system_score_gemma":0.00008708212,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001242911,"about_ca_topic_score_gemma":0.0008473182,"domain_scores_codex":[0.9988596,0.00006279168,0.0002758044,0.0004919278,0.00008866804,0.0002212174],"domain_scores_gemma":[0.9991167,0.0000328287,0.00005168472,0.0003476709,0.0003770407,0.00007411978],"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":[0.00009500309,0.00007080552,0.08754066,0.0007153391,0.0004671786,0.00000281033,0.00114431,0.9085864,0.0002869151,0.00003210438,0.000004745856,0.001053723],"study_design_scores_gemma":[0.0003317183,0.00002488121,0.1021037,0.00002464107,0.000389819,0.000008774156,0.001218247,0.8950894,0.0005555241,0.00003945669,0.00001381566,0.0002000058],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7680625,0.0003521362,0.2311112,0.00003348448,0.00006731904,0.000211741,0.00001987077,0.0001087871,0.00003299416],"genre_scores_gemma":[0.9963767,0.00007084652,0.003342851,0.000004401314,0.00003449,0.00003646502,0.00003962087,0.00001585706,0.00007875935],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2283142,"threshold_uncertainty_score":0.6399534,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003502055118739479,"score_gpt":0.1897852621761768,"score_spread":0.1862832070574374,"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."}}