{"id":"W2156623102","doi":"10.1111/j.1745-4530.2009.00552.x","title":"RESPONSE SURFACE METHODOLOGY APPLIED TO THE EXTRACTION OF PHENOLIC COMPOUNDS FROM<i>JATROPHA CURCAS</i>LINN. LEAVES USING SUPERCRITICAL CO<sub>2</sub>WITH A METHANOL CO‐SOLVENT","year":2009,"lang":"en","type":"article","venue":"Journal of Food Process Engineering","topic":"Essential Oils and Antimicrobial Activity","field":"Agricultural and Biological Sciences","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Royal Golden Jubilee (RGJ) Ph.D. Programme","keywords":"Methanol; Chemistry; Aqueous solution; Response surface methodology; Extraction (chemistry); Supercritical fluid; Chromatography; Gallic acid; Toluene; Solvent; Ellagic acid; Box–Behnken design; Jatropha curcas; Nuclear chemistry; Organic chemistry; Botany; Polyphenol; Antioxidant","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":[0.0007729516,0.0001836425,0.0004344135,0.00003791363,0.000108451,0.00005574638,0.0002477173,0.0001185055,0.000007467031],"category_scores_gemma":[0.0001186597,0.00007492056,0.00012135,0.000333052,0.00004036059,0.0001937469,0.00001741625,0.0004166731,9.357177e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003372599,"about_ca_system_score_gemma":0.00003078973,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001746052,"about_ca_topic_score_gemma":0.00001290851,"domain_scores_codex":[0.9987084,0.0001474082,0.000406198,0.0001787994,0.000288673,0.0002704787],"domain_scores_gemma":[0.9988326,0.0006877361,0.0001370718,0.00005436939,0.000170682,0.000117533],"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":[0.0009595893,0.0001318713,0.0000316204,0.00001844976,0.0000590948,0.000008755776,0.0002222445,0.01325845,0.9836327,0.00003104102,0.00001192988,0.001634275],"study_design_scores_gemma":[0.0002307264,0.001039629,0.00388375,0.0001017108,0.00006997523,0.0001281481,0.0002061882,0.0002633982,0.9936783,0.00006956505,0.000169737,0.0001588729],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9934916,0.0002107896,0.004890283,0.001124544,0.00009894494,0.0001286783,0.00002451655,0.00001914892,0.0000114529],"genre_scores_gemma":[0.9966074,0.00003074399,0.00295287,0.00013476,0.0002670328,0.000001110057,0.000002240578,0.000003325061,5.365099e-7],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01299506,"threshold_uncertainty_score":0.3055172,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03962163347616748,"score_gpt":0.2845682040982793,"score_spread":0.2449465706221118,"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."}}