{"id":"W4244943878","doi":"10.1515/iupac.88.0171","title":"Continuous Extraction","year":2017,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"Analytical Chemistry and Chromatography","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; National Research Council Canada","funders":"","keywords":"Computer science; Extraction (chemistry); Sample (material); Process engineering; Scale (ratio); Throughput; Sample preparation; Chromatography; Chemistry; Engineering; Physics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003110294,0.003113158,0.002402761,0.004326255,0.001235398,0.003484482,0.003487779,0.002282513,0.07109077],"category_scores_gemma":[0.01278481,0.0007079954,0.002407118,0.007489575,0.0005036824,0.002395068,0.002707831,0.002287251,0.1132254],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00163896,"about_ca_system_score_gemma":0.004528827,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009678719,"about_ca_topic_score_gemma":0.01648626,"domain_scores_codex":[0.9961455,0.0006851138,0.0005754351,0.001454838,0.0007967182,0.0003423353],"domain_scores_gemma":[0.9945709,0.001640633,0.0007696518,0.001280497,0.001504915,0.0002333721],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0006866801,0.00007879567,0.003430346,0.008822509,0.0003446774,0.0000745989,0.00005394625,0.0007663729,0.001269442,0.001873574,0.9472054,0.03539371],"study_design_scores_gemma":[0.0002813322,0.00004930515,0.003881105,0.001109862,0.0001474191,0.00009386306,0.00005216271,0.0003345095,0.001180377,0.002400527,0.990426,0.00004350882],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0002657921,0.0005231663,0.0006433277,0.00008468872,0.00005036045,0.00007319629,0.996179,0.0006964441,0.001484078],"genre_scores_gemma":[0.000557515,0.0004649788,0.001479796,0.0001187461,0.00001618594,0.0002742305,0.9959574,0.0001204391,0.001010568],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.07109077,"threshold_uncertainty_score":0.2378223,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01277136091636973,"score_gpt":0.4058967966079162,"score_spread":0.3931254356915465,"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."}}