{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000502252,0.0007499841,0.0007119753,0.0002811069,0.0001431766,0.0003594209,0.0002901642,0.0005045754,0.000522821],"category_scores_gemma":[0.0003630678,0.0003100998,0.001078385,0.0004440101,0.0002265644,0.0001804993,0.0001532244,0.0004164002,0.000348346],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003971455,"about_ca_system_score_gemma":0.0003243755,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001771222,"about_ca_topic_score_gemma":0.001914824,"domain_scores_codex":[0.9996496,0.00009991194,0.00002693123,0.00005750783,0.0001325066,0.00003353794],"domain_scores_gemma":[0.9998308,0.00006087619,0.0000311391,0.00001106151,0.00005668314,0.000009328992],"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":[0.00008388571,0.00002800948,0.000174756,0.0001477378,0.00002236738,0.00003223385,0.00002264554,0.002251887,0.9931749,0.00005095573,0.00003923198,0.003971396],"study_design_scores_gemma":[0.00001961747,0.0004712811,0.002043299,0.000006233084,0.00003541133,0.00006418165,0.00002924372,0.02397173,0.9724277,0.00005796085,0.0008530893,0.0000203311],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7792645,0.001864819,0.2155854,0.0001822126,0.00009331163,0.0003427913,0.000409928,0.000701152,0.001555922],"genre_scores_gemma":[0.8936228,0.001595082,0.1015555,0.00008754471,0.00001310898,0.0005461371,0.0003431043,0.00004864467,0.002188038],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001771222,"threshold_uncertainty_score":0.0035218,"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."}}