{"id":"W4382724029","doi":"10.3390/pr11071953","title":"Optimization of Supercritical Carbon Dioxide Fluid Extraction of Medicinal Cannabis from Quebec","year":2023,"lang":"en","type":"article","venue":"Processes","topic":"Cannabis and Cannabinoid Research","field":"Medicine","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Collège de Maisonneuve; Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Cannabinol; Supercritical carbon dioxide; Supercritical fluid; Yield (engineering); Extraction (chemistry); Raw material; Box–Behnken design; Cannabis; Chromatography; Chemistry; Response surface methodology; Medicine; Materials science; Cannabidiol; Organic chemistry; Composite material","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001898058,0.000102021,0.0003006612,0.0002091193,0.00002707344,0.000006330644,0.00008013393,0.00009200587,0.0002458391],"category_scores_gemma":[0.001201507,0.00008689817,0.00005099504,0.0007884966,0.0001715928,0.000085979,0.00003324844,0.0001295388,0.000003359969],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006005068,"about_ca_system_score_gemma":0.0007603117,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01963412,"about_ca_topic_score_gemma":0.001655958,"domain_scores_codex":[0.9986385,0.00002701816,0.0003268856,0.0002112118,0.0005804367,0.0002159254],"domain_scores_gemma":[0.9989019,0.00003724562,0.00003040385,0.0001797014,0.0007277579,0.0001229631],"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":[0.001800341,0.0009634753,0.05405477,0.01485833,0.0002613425,0.0001813365,0.003687769,0.002870112,0.8681279,0.0002006662,0.04143882,0.01155516],"study_design_scores_gemma":[0.001871915,0.0006987734,0.05939531,0.0008062703,0.0002248747,0.00004277674,0.003341823,0.008396832,0.9166881,0.000190815,0.008084645,0.0002578059],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9878129,0.001760932,0.0004686087,0.008285775,0.0001087701,0.0002452816,0.00003681709,0.00007209706,0.001208778],"genre_scores_gemma":[0.996081,0.0007077176,0.000177879,0.00004752624,0.0001850183,0.00004709461,0.0001140618,0.00002347359,0.00261623],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04856029,"threshold_uncertainty_score":0.9868942,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01934167533224824,"score_gpt":0.3164998868468277,"score_spread":0.2971582115145795,"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."}}