{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001138427,0.001103216,0.0008107863,0.001253648,0.0004108192,0.0007444889,0.0005982193,0.0009621177,0.002239118],"category_scores_gemma":[0.003157866,0.0004304666,0.0009002027,0.0009534195,0.0004287002,0.0007528253,0.0005178844,0.001036843,0.002104354],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008300391,"about_ca_system_score_gemma":0.0007200405,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004050512,"about_ca_topic_score_gemma":0.005427633,"domain_scores_codex":[0.9994667,0.00005713077,0.0000332993,0.0001639099,0.0002244504,0.00005456054],"domain_scores_gemma":[0.9987539,0.0006735055,0.0001901571,0.0001133302,0.0002269662,0.00004219828],"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":[0.002748385,0.0008480268,0.03447393,0.0008094432,0.0003145519,0.0004806615,0.00007578395,0.1878958,0.4717544,0.001530207,0.00465344,0.2944153],"study_design_scores_gemma":[0.00003499693,0.0002758835,0.01212556,0.00002495807,0.00008545697,0.0001653964,0.00002597947,0.8302183,0.1537539,0.001215168,0.002016324,0.00005816671],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5801953,0.00388641,0.4015312,0.0007649012,0.0001056059,0.0002427568,0.004550607,0.005151527,0.003571613],"genre_scores_gemma":[0.8992789,0.002238836,0.09013393,0.0002319077,0.00006590982,0.0001473065,0.003149573,0.0003045896,0.004449009],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004050512,"threshold_uncertainty_score":0.008053899,"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."}}