{"id":"W4381951076","doi":"10.1016/j.jfoodeng.2023.111637","title":"Modeling and optimization of the extraction of ylang-ylang essential oils using surrogate models from simulated data, coupled with covariance matrix adaptation evolution strategy","year":2023,"lang":"en","type":"article","venue":"Journal of Food Engineering","topic":"Process Optimization and Integration","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Process engineering; Engineering","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":[0.0001832671,0.0001090183,0.0001910368,0.0001765843,0.00003174677,0.00002789374,0.0001154218,0.0000709783,0.000002918507],"category_scores_gemma":[0.00004608166,0.00009053032,0.00002835261,0.0004131331,0.00001007513,0.0009672268,0.00001659249,0.0001417127,8.482683e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005670406,"about_ca_system_score_gemma":0.0000519495,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006191615,"about_ca_topic_score_gemma":0.00001448937,"domain_scores_codex":[0.9991289,0.000015217,0.0004407362,0.00008772494,0.0002288746,0.00009859751],"domain_scores_gemma":[0.9993371,0.00003812227,0.0002108306,0.0001306705,0.0002523664,0.00003088488],"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":[0.0000347824,0.000007297409,0.000006010835,0.0000750876,0.0000711073,6.382214e-7,0.0001721545,0.9392852,0.06019889,0.00007688023,0.000001046446,0.0000709415],"study_design_scores_gemma":[0.0004657954,0.00004203532,0.00002893276,0.0002535178,0.0000741407,0.000009958121,0.0001698636,0.9929993,0.005817055,0.00005533285,2.217387e-7,0.00008381291],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3915429,0.0002697636,0.6079673,0.000004146332,0.0001095421,0.00005494309,0.00001821947,0.00003087992,0.000002404174],"genre_scores_gemma":[0.9676245,0.0001568171,0.03210229,5.980161e-7,0.00004972283,5.662528e-7,0.00004019597,0.00002430667,9.422283e-7],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5760817,"threshold_uncertainty_score":0.3691719,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03278658687767466,"score_gpt":0.2503068929947975,"score_spread":0.2175203061171228,"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."}}