{"id":"W2114903657","doi":"10.1111/j.1745-4530.2002.tb00571.x","title":"MODELING AND OPTIMIZATION OF CONSTANT RETORT TEMPERATURE (CRT) THERMAL PROCESSING USING COUPLED NEURAL NETWORKS AND GENETIC ALGORITHMS","year":2002,"lang":"en","type":"article","venue":"Journal of Food Process Engineering","topic":"Food Drying and Modeling","field":"Agricultural and Biological Sciences","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Retort; Algorithm; Artificial neural network; Thermal diffusivity; Genetic algorithm; Thermal conduction; Thermal; Generalization; Mathematics; Computer science; Materials science; Biological system; Thermodynamics; Engineering; Mathematical optimization; Artificial intelligence; Physics; Mathematical analysis; Composite material","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":[0.0008554021,0.0008852422,0.0007545714,0.0006457625,0.0003631221,0.0009474303,0.000708989,0.001041493,0.0008801533],"category_scores_gemma":[0.001193741,0.0006049111,0.0008235839,0.0004988163,0.0005778375,0.0005931256,0.000505391,0.0005874556,0.0001402511],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001304336,"about_ca_system_score_gemma":0.001177084,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01641912,"about_ca_topic_score_gemma":0.0103388,"domain_scores_codex":[0.9997326,0.00008610821,0.0000130239,0.00006951477,0.00006009023,0.00003881212],"domain_scores_gemma":[0.9994679,0.0003196912,0.00007175201,0.00002083096,0.0001003979,0.00001938858],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000009917163,0.000008178731,0.0001054189,0.00000587051,0.000004803838,0.000006571061,0.000004532011,0.9981035,0.0003654115,0.00009837374,0.00001281418,0.001274557],"study_design_scores_gemma":[0.000001585674,0.00001004896,0.00004318376,8.471644e-7,0.000002050442,6.70439e-7,0.000001533167,0.9996786,0.0001631052,0.00007671046,0.00002045541,0.000001162964],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4575935,0.0004718881,0.5342487,0.0001522292,0.00004080914,0.0001463032,0.00009043194,0.0003551244,0.006901043],"genre_scores_gemma":[0.9572302,0.0001441213,0.04009685,0.00003030316,0.000006006102,0.0001846919,0.00006267396,0.00002786798,0.002217249],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01641912,"threshold_uncertainty_score":0.03264707,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02139992060022933,"score_gpt":0.2054017543283295,"score_spread":0.1840018337281002,"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."}}