{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001220132,0.0001265188,0.0002197951,0.00003005198,0.00009360699,0.00008180716,0.00007531221,0.0000858393,0.00000315056],"category_scores_gemma":[0.00002998499,0.00006061902,0.00003475917,0.0001623413,0.00001994178,0.0002733001,0.00001847348,0.0001931008,6.385155e-9],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009605372,"about_ca_system_score_gemma":0.000006442344,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005060618,"about_ca_topic_score_gemma":0.000001301748,"domain_scores_codex":[0.9991971,0.00001090169,0.0003459306,0.0001202094,0.0001601873,0.0001656412],"domain_scores_gemma":[0.9995266,0.0000242214,0.0001581402,0.00001994025,0.0001911179,0.00007997172],"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":[0.00001215965,0.00001120092,0.0001073258,0.0000516551,0.00001543716,0.000003813066,0.0001538183,0.9430124,0.05038564,8.295946e-7,1.091721e-7,0.006245576],"study_design_scores_gemma":[0.0001754205,0.0002296351,0.0001080114,0.0002200673,0.00003849159,0.0002091943,0.0001362105,0.9982277,0.0005347333,0.000003197279,3.248318e-7,0.000117002],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9695162,0.0100635,0.02025122,0.0000317694,0.00005929099,0.00005917682,0.000001265699,0.00001563976,0.000001988335],"genre_scores_gemma":[0.9937474,0.0002253883,0.005817169,0.00001135384,0.0001937267,5.823224e-7,6.85866e-7,0.000003178066,5.493469e-7],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05521527,"threshold_uncertainty_score":0.2471972,"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."}}