{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004997072,0.00009511887,0.0002056657,0.00002658761,0.00008629805,0.00006284523,0.0005591756,0.00004345394,0.00006704805],"category_scores_gemma":[0.00008046047,0.00002647598,0.0001259069,0.0007971324,0.0001113331,0.0004110475,0.00008014157,0.0001435391,0.00001314307],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004045447,"about_ca_system_score_gemma":0.00001807893,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003913967,"about_ca_topic_score_gemma":0.00001410131,"domain_scores_codex":[0.9985785,0.00002055054,0.0003811456,0.0001387056,0.0006300129,0.0002510172],"domain_scores_gemma":[0.9988258,0.00008217434,0.0003984105,0.00003959184,0.0005278847,0.0001261776],"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":[0.000007420626,0.00003479353,0.002422386,0.000003848281,0.000003741134,7.480228e-7,0.0001016309,0.0004366224,0.9897916,0.0001045732,0.00006360207,0.007028996],"study_design_scores_gemma":[0.0001712728,0.001039357,0.7094388,0.0001270995,0.00001524303,0.0001234684,0.001374046,0.00008552055,0.2864823,0.00007474437,0.0009027314,0.0001653948],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9974371,0.0001404129,0.000004392877,0.0004857782,0.0004072364,0.00006123484,0.000001945011,0.000008501746,0.001453463],"genre_scores_gemma":[0.9990389,0.00002487836,0.0005343081,0.00004420981,0.0001833892,2.167609e-7,6.872747e-7,3.416572e-7,0.0001730695],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7070164,"threshold_uncertainty_score":0.1079659,"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."}}