{"id":"W2936522167","doi":"10.5539/jas.v11n5p250","title":"Drying Kinetics of Noni Seeds","year":2019,"lang":"en","type":"article","venue":"Journal of Agricultural Science","topic":"Food Drying and Modeling","field":"Agricultural and Biological Sciences","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Instituto Federal Goiás; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Fundação de Amparo à Pesquisa do Estado de Goiás; Financiadora de Estudos e Projetos; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Akaike information criterion; Equilibrium moisture content; Mathematics; Coefficient of determination; Thermodynamics; Relative humidity; Arrhenius equation; Moisture; Water content; Activation energy; Bayesian information criterion; Diffusion; Chemistry; Statistics; Materials science; Physics; Engineering; Composite material; Physical chemistry","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":[0.0004133531,0.0003537504,0.0003797185,0.0002748257,0.0001317639,0.0003529423,0.0003797001,0.0002360381,0.0008583419],"category_scores_gemma":[0.0009953936,0.000159726,0.0005863101,0.0003084289,0.0001694704,0.0005721094,0.0001538973,0.0005434614,0.0002554189],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003664159,"about_ca_system_score_gemma":0.00016088,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001356769,"about_ca_topic_score_gemma":0.001205659,"domain_scores_codex":[0.9997744,0.00002562808,0.00002149511,0.00006814189,0.00009194641,0.00001848638],"domain_scores_gemma":[0.9996431,0.0001548342,0.00007862377,0.00002590715,0.00008112063,0.00001631184],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003615466,0.0001086303,0.00500965,0.0007249068,0.00004831581,0.0002315713,0.0002761741,0.01071849,0.9650109,0.001284112,0.0003818002,0.01584397],"study_design_scores_gemma":[0.00002036753,0.0004739167,0.02399394,0.00005708877,0.00006675735,0.0003558036,0.0001569397,0.125325,0.8440177,0.0008182734,0.004655206,0.00005908133],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9644836,0.003054841,0.02838024,0.0001244737,0.0000648829,0.00009513796,0.0005622495,0.0001085476,0.003125916],"genre_scores_gemma":[0.9837343,0.002757244,0.009650814,0.00005652855,0.000007007741,0.00007080221,0.00056666,0.00005711525,0.00309944],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001356769,"threshold_uncertainty_score":0.002871394,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0166174538601655,"score_gpt":0.2202422124294182,"score_spread":0.2036247585692527,"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."}}