{"id":"W3127940896","doi":"10.1002/er.6498","title":"Energy and environmental enhancement of power generation units by means of <scp>zero‐flow</scp> coolant strategy","year":2021,"lang":"en","type":"article","venue":"International Journal of Energy Research","topic":"Thermodynamic and Exergetic Analyses of Power and Cooling Systems","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta; Concordia University","funders":"","keywords":"Zero emission; Coolant; Electricity generation; Process engineering; Environmental science; Combustion; Electricity; Environmental pollution; Automotive engineering; Energy consumption; Internal combustion engine; Fuel efficiency; Power (physics); Nuclear engineering; Waste management; Engineering; Mechanical engineering; Thermodynamics; Chemistry; Electrical engineering; Physics","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":[0.0001828953,0.0003292939,0.000252029,0.0003222337,0.0001825721,0.0003405964,0.0003105217,0.0001301791,0.001224567],"category_scores_gemma":[0.0002134453,0.00007536965,0.0002334452,0.0002160318,0.0002060805,0.0003904034,0.0001776374,0.0001772107,0.0001319667],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001767551,"about_ca_system_score_gemma":0.0002693266,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008933145,"about_ca_topic_score_gemma":0.001272217,"domain_scores_codex":[0.999895,0.00001928623,0.000005242562,0.00002216476,0.00003251314,0.0000257817],"domain_scores_gemma":[0.9999374,0.00001393618,0.00001408785,0.00001028377,0.00001935973,0.000005036751],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001035814,0.0004796825,0.00868322,0.0008534644,0.00006088506,0.0003448917,0.000153581,0.07895494,0.671918,0.006636014,0.00104403,0.2298354],"study_design_scores_gemma":[0.00006365603,0.002280868,0.02651553,0.00004819061,0.00009951465,0.0002396514,0.0001864795,0.1727584,0.7880619,0.001343139,0.008366518,0.0000361745],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9188566,0.0005892883,0.07062045,0.0000784397,0.00004117383,0.00008334445,0.00007584655,0.0002176245,0.009437132],"genre_scores_gemma":[0.9948041,0.00007617111,0.004532874,0.000005737485,0.000002842212,0.00001046503,0.0000223605,0.000008238781,0.0005372699],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001224567,"threshold_uncertainty_score":0.004096627,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02120421649151856,"score_gpt":0.2691921380949025,"score_spread":0.247987921603384,"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."}}