{"id":"W4251535793","doi":"10.1515/iupac.88.0248","title":"Stage (Liquid–Liquid Extraction)","year":2017,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"Analytical chemistry methods development","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; National Research Council Canada","funders":"","keywords":"Extraction (chemistry); Computer science; Process engineering; Stage (stratigraphy); Scale (ratio); Sample (material); Throughput; 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":[0.003111528,0.002421377,0.002060344,0.004117053,0.0009746482,0.002869903,0.003178908,0.001654035,0.0687591],"category_scores_gemma":[0.01008245,0.0006258097,0.002650279,0.006884873,0.0004530088,0.001737579,0.003043845,0.002178358,0.08638039],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001492958,"about_ca_system_score_gemma":0.004335022,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009253353,"about_ca_topic_score_gemma":0.01793834,"domain_scores_codex":[0.9964774,0.0007098021,0.0007038832,0.001222148,0.0005682889,0.0003185441],"domain_scores_gemma":[0.9951512,0.001660742,0.000919195,0.001020433,0.001060109,0.0001884467],"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":[0.000890894,0.00007904947,0.004938703,0.01799292,0.0006179941,0.0000888189,0.00007582231,0.0009108423,0.001518861,0.001989684,0.9385103,0.03238599],"study_design_scores_gemma":[0.0004137524,0.00005724845,0.006012078,0.001479409,0.0002307392,0.00009726168,0.00004959078,0.0003595374,0.001294807,0.001984844,0.9879759,0.00004479658],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.000216381,0.0003345378,0.0004460327,0.00005676838,0.00002788385,0.00009348943,0.9974191,0.0005069816,0.0008988374],"genre_scores_gemma":[0.0006191721,0.0003892879,0.001771384,0.0001167474,0.00001241014,0.0004797602,0.9956509,0.0001182383,0.0008420547],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.0687591,"threshold_uncertainty_score":0.2300221,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03362579156315015,"score_gpt":0.4592548289441459,"score_spread":0.4256290373809958,"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."}}