{"id":"W4389084237","doi":"10.1039/d3dd00176h","title":"Extraction yield prediction for the large-scale recovery of cannabinoids","year":2023,"lang":"en","type":"article","venue":"Digital Discovery","topic":"Analytical Chemistry and Chromatography","field":"Chemistry","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Yield (engineering); Extraction (chemistry); Scale (ratio); Chromatography; Environmental science; Chemistry; Materials science; Geography; Metallurgy; Cartography","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":[0.00007371443,0.0001183442,0.0001343259,0.00003066429,0.00009942024,0.0001118578,0.0001464727,0.0001092404,0.0001079736],"category_scores_gemma":[0.000158366,0.00009009657,0.0002944875,0.0002395317,0.00007630757,0.0006182471,0.00004614469,0.0001081015,0.00001502887],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002071394,"about_ca_system_score_gemma":0.00002975294,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006982835,"about_ca_topic_score_gemma":0.00000297612,"domain_scores_codex":[0.9991294,0.0000018649,0.0002393166,0.000206181,0.0001889337,0.0002342987],"domain_scores_gemma":[0.9992732,0.0002922149,0.00008458886,0.000263782,0.00004132044,0.00004493351],"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":[0.001501062,0.001883662,0.08962163,0.004147866,0.00158153,0.00002982651,0.0009310961,0.00262253,0.5932634,0.004606233,0.2675792,0.03223193],"study_design_scores_gemma":[0.001682867,0.0002143848,0.02339109,0.0005293632,0.0004247202,0.00002009229,0.009674478,0.01173338,0.8501388,0.01161599,0.08966482,0.0009100426],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9458092,0.0001028687,0.005632299,0.0002031001,0.0001954584,0.0001028471,0.002680715,0.0001720489,0.04510142],"genre_scores_gemma":[0.9840615,0.00004240128,0.000005608768,0.00002393808,0.000171481,0.00004963217,0.0004334,0.00001752436,0.01519452],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2568753,"threshold_uncertainty_score":0.3674031,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0135724673066085,"score_gpt":0.2430088995539108,"score_spread":0.2294364322473023,"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."}}