{"id":"W57426425","doi":"10.1007/978-1-4419-7475-4_16","title":"Bioseparation of Nutraceuticals Using Supercritical Carbon Dioxide","year":2010,"lang":"en","type":"book-chapter","venue":"Food engineering series","topic":"Phase Equilibria and Thermodynamics","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Nutraceutical; Supercritical carbon dioxide; Extraction (chemistry); Fractionation; Supercritical fluid; Solubility; Supercritical fluid extraction; Chemistry; Chromatography; Process engineering; Organic chemistry; Food science; 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00006187452,0.0004171612,0.0005135824,0.0001810463,0.00001975148,0.00002060927,0.0001600358,0.0005997425,0.00003896383],"category_scores_gemma":[0.00002543057,0.0004859061,0.0001403834,0.00004141514,0.00008933142,0.0001481548,0.00003854913,0.0005674759,0.00000441467],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007211511,"about_ca_system_score_gemma":0.00002838907,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002339765,"about_ca_topic_score_gemma":0.00001119234,"domain_scores_codex":[0.9988821,0.000003263245,0.0004103068,0.0002044875,0.0001991072,0.0003007812],"domain_scores_gemma":[0.9994159,0.00004801081,0.0000172107,0.0003579751,0.00005073498,0.0001102209],"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":[0.00001327817,0.000009717292,0.000002386952,0.0006382159,0.0002359951,0.0000135431,0.0001046634,0.01510051,0.8172811,0.1665294,0.00000189265,0.00006923265],"study_design_scores_gemma":[0.0005785925,0.0005020989,0.00003602117,0.001230295,0.000525928,0.0002305777,0.00002095971,0.2742907,0.6810568,0.01214556,0.02697496,0.002407502],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8878785,0.002387018,0.005530721,0.00002440215,0.0029935,0.0005121883,0.0003922855,0.00139854,0.09888284],"genre_scores_gemma":[0.9941824,0.0001314081,0.003667071,0.000003383137,0.0004288967,0.000008490788,0.00006318194,0.0002908138,0.001224365],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2591902,"threshold_uncertainty_score":0.9997593,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01391156766871994,"score_gpt":0.2087011582032705,"score_spread":0.1947895905345505,"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."}}