{"id":"W4312126227","doi":"10.3390/molecules27248780","title":"Cold Ethanol Extraction of Cannabinoids and Terpenes from Cannabis Using Response Surface Methodology: Optimization and Comparative Study","year":2022,"lang":"en","type":"article","venue":"Molecules","topic":"Cannabis and Cannabinoid Research","field":"Medicine","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada; McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Extraction (chemistry); Response surface methodology; Yield (engineering); Chromatography; Chemistry; Central composite design; Ethanol; Solvent; Terpene; Dry matter; Principal component analysis; Botany; Materials science; Mathematics; Biochemistry; Biology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0009315936,0.0001501767,0.0004040507,0.0001735731,0.0002324248,0.00002441911,0.00006548445,0.00005626593,0.0001105277],"category_scores_gemma":[0.0001424383,0.0001472345,0.00003815901,0.0002868528,0.0001706579,0.00006613453,0.0001873013,0.0002552396,1.842624e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001266219,"about_ca_system_score_gemma":0.0002335006,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004816523,"about_ca_topic_score_gemma":0.0001184603,"domain_scores_codex":[0.9975159,0.001288241,0.0002687054,0.0003636205,0.000371057,0.000192436],"domain_scores_gemma":[0.9992983,0.00007697166,0.0001269189,0.0002162361,0.0001680732,0.0001135536],"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":[0.003929762,0.0004137456,0.01633968,0.00004121435,0.0001528259,0.00008959682,0.005978913,0.02286457,0.9481562,0.00003045842,0.001738491,0.0002645859],"study_design_scores_gemma":[0.008884628,0.007601995,0.3264343,0.00008953581,0.0007736884,0.0003432321,0.06730451,0.08627427,0.4938966,0.00008114921,0.0075042,0.0008118604],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9935654,0.001218455,0.001537994,0.002648185,0.00007203585,0.0008181033,0.00008896049,0.00002113068,0.00002977829],"genre_scores_gemma":[0.9918477,0.00002236574,0.006550236,0.00009102703,0.00001868504,0.00006273086,0.0000183273,0.0000192172,0.001369688],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4542595,"threshold_uncertainty_score":0.7281175,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.101180417033654,"score_gpt":0.3857910928827292,"score_spread":0.2846106758490752,"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."}}