{"id":"W3184977222","doi":"10.5539/eer.v11n2p19","title":"Energy and Exergy Analysis of an Indirect-Mode Natural Convection Solar Dryer for Maize","year":2021,"lang":"en","type":"article","venue":"Energy and Environment Research","topic":"Solar Thermal and Photovoltaic Systems","field":"Energy","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Exergy; Exergy efficiency; Environmental science; Solar dryer; Energy analysis; Grain drying; Moisture; Solar energy; Materials science; Environmental engineering; Waste management; Energy (signal processing); Composite material; Mathematics; Ecology; Engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002061312,0.0002836261,0.000519751,0.0003579855,0.0003163458,0.0002892499,0.0004437452,0.000199884,0.001280954],"category_scores_gemma":[0.0002312812,0.0001401387,0.0005242015,0.0003858042,0.0001716755,0.0002964437,0.0002434567,0.0002508427,0.0002016538],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004769212,"about_ca_system_score_gemma":0.0002401242,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001570313,"about_ca_topic_score_gemma":0.003026039,"domain_scores_codex":[0.9998879,0.000008385693,0.000007016807,0.0000210943,0.00006317005,0.00001238711],"domain_scores_gemma":[0.9999194,0.00003468604,0.00001234252,0.000007305898,0.00001940929,0.000006705618],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0008062104,0.00009258957,0.006625587,0.0003119836,0.00003619803,0.000139648,0.00008922639,0.02118704,0.9505604,0.0001899778,0.00008502528,0.0198761],"study_design_scores_gemma":[0.0001290792,0.001335644,0.06811966,0.00001341939,0.0001018135,0.0001682765,0.0001506543,0.07156226,0.855544,0.0002559588,0.002577594,0.00004162853],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9938452,0.0001394943,0.005054377,0.00001456585,0.000003107746,0.00002604957,0.000164802,0.00002965357,0.0007227242],"genre_scores_gemma":[0.9952587,0.0001381187,0.003143986,0.000005962907,0.000002042124,0.00002304653,0.0002022302,0.00001660993,0.001209219],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001570313,"threshold_uncertainty_score":0.004285276,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02803447340013249,"score_gpt":0.2860336762268795,"score_spread":0.257999202826747,"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."}}