{"id":"W1990016423","doi":"10.1016/j.jfoodeng.2009.10.001","title":"Genetic algorithm optimization of supercritical fluid extraction of nimbin from neem seeds","year":2009,"lang":"en","type":"article","venue":"Journal of Food Engineering","topic":"Hibiscus Plant Research Studies","field":"Pharmacology, Toxicology and Pharmaceutics","cited_by":31,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University; University of Waterloo","funders":"","keywords":"Genetic algorithm; Mathematical optimization; Supercritical fluid; Supercritical carbon dioxide; Extraction (chemistry); Algorithm; Mathematics; Supercritical fluid extraction; Computer science; Chemistry; Chromatography; Thermodynamics; Physics","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":[0.0003552277,0.0001156176,0.0003134396,0.0002152859,0.00003192876,0.00000611486,0.0001364337,0.0001456528,0.0001318619],"category_scores_gemma":[0.0001929267,0.0001060545,0.0001041441,0.0001614836,0.00003755094,0.0001362791,0.00001800864,0.0005022681,0.000001363397],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005013002,"about_ca_system_score_gemma":0.00004498977,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003863608,"about_ca_topic_score_gemma":5.197083e-7,"domain_scores_codex":[0.9988565,0.00007750562,0.0005315823,0.00007849373,0.0002454834,0.0002104154],"domain_scores_gemma":[0.9992046,0.0003051863,0.00007341462,0.00006977114,0.0002227309,0.0001243188],"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":[0.0000963453,0.0001889827,0.0003365764,0.00003933428,0.0002513969,0.00004015494,0.0002907038,0.4361517,0.5552361,0.00003843871,0.0002708199,0.00705948],"study_design_scores_gemma":[0.0018507,0.001994771,0.01622498,0.00014165,0.000292583,0.0002554576,0.0001705572,0.3798899,0.5975851,0.00009556219,0.001279052,0.0002196883],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8974972,0.002424296,0.09902755,0.0002457052,0.0005058584,0.00009948223,0.00005224528,0.00001570123,0.00013191],"genre_scores_gemma":[0.9684548,0.0008257888,0.03039349,0.00002421415,0.0002810062,0.000001035581,0.000003502243,0.000009300134,0.000006902118],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07095751,"threshold_uncertainty_score":0.4324777,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05461540912301872,"score_gpt":0.3836796097091369,"score_spread":0.3290642005861182,"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."}}