{"id":"W2892038837","doi":"10.5539/jas.v10n10p504","title":"Isotherms and Isosteric Heat Desorption of Hymenaea stigonocarpa Mart. Seeds","year":2018,"lang":"en","type":"article","venue":"Journal of Agricultural Science","topic":"Agricultural and Food Sciences","field":"Agricultural and Biological Sciences","cited_by":18,"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; Water content; Equilibrium moisture content; Bayesian information criterion; Desiccant; Desorption; Relative humidity; Humidity; Moisture; Mathematics; Thermodynamics; Environmental science; Statistics; Botany; Soil science; Chemistry; Physics; Meteorology; Biology; Geology; Geotechnical engineering; Sorption","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":[0.0001315143,0.0001934108,0.0001466862,0.0003538831,0.00008814198,0.0001842817,0.0001338105,0.0001391734,0.0005538756],"category_scores_gemma":[0.0002603825,0.00008097575,0.0002506985,0.0002213744,0.0001002799,0.0001906041,0.00007664389,0.0002023271,0.0001462156],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002099495,"about_ca_system_score_gemma":0.00009530225,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004607962,"about_ca_topic_score_gemma":0.004069664,"domain_scores_codex":[0.9999412,0.000006526052,0.0000030848,0.00001955074,0.00002189879,0.000007637502],"domain_scores_gemma":[0.9999149,0.00003450791,0.00001661234,0.000005803474,0.00002104742,0.000007116264],"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":[0.000152577,0.00003756692,0.01233777,0.0001362292,0.00002208913,0.0001088588,0.0001172339,0.004513326,0.9753761,0.0002176305,0.00008380574,0.006896806],"study_design_scores_gemma":[0.00001275602,0.0004869713,0.2086139,0.00002261626,0.00005885342,0.0003388486,0.0003831769,0.08682423,0.6999984,0.0004908764,0.002732873,0.00003641784],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9968251,0.0004503694,0.001510675,0.0000161992,0.000003676083,0.000007189291,0.000342089,0.00001948163,0.0008251868],"genre_scores_gemma":[0.9984446,0.0002227725,0.0005135642,0.000005204721,0.000001332874,0.000006304233,0.0002651678,0.000005991038,0.0005350662],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004607962,"threshold_uncertainty_score":0.009162307,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01624345203684418,"score_gpt":0.2156077997578659,"score_spread":0.1993643477210217,"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."}}