{"id":"W4240265790","doi":"10.1515/iupac.88.0234","title":"Continuous Liquid–Liquid Extraction","year":2017,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"Microfluidic and Capillary Electrophoresis Applications","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; National Research Council Canada","funders":"","keywords":"Extraction (chemistry); Computer science; Process engineering; Sample preparation; Sample (material); Scale (ratio); Chromatography; Microwave; 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":[0.003255368,0.002601172,0.002446274,0.005346389,0.00109009,0.002902415,0.00309811,0.002182898,0.04061673],"category_scores_gemma":[0.01364606,0.0005969965,0.002211359,0.009125115,0.0005121453,0.001767663,0.002626862,0.002156704,0.05712222],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001670578,"about_ca_system_score_gemma":0.004871571,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01063165,"about_ca_topic_score_gemma":0.02026673,"domain_scores_codex":[0.9959795,0.0007556687,0.0008193644,0.001333484,0.0007739224,0.0003380516],"domain_scores_gemma":[0.9945943,0.001900478,0.0008327253,0.001025804,0.001401947,0.0002448105],"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":[0.0006385841,0.00008178484,0.004515554,0.01485252,0.0004710411,0.0001203108,0.00006324865,0.0008951061,0.001232,0.001714549,0.9448844,0.03053079],"study_design_scores_gemma":[0.0003512472,0.00005071748,0.006100677,0.001753176,0.0002160709,0.0001250384,0.00006447478,0.0003101964,0.001086505,0.002172446,0.9877177,0.00005156684],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0002667087,0.0006882285,0.0003929576,0.00008140001,0.00003875239,0.00006929067,0.9971342,0.0003089818,0.001019455],"genre_scores_gemma":[0.0004908976,0.0004962583,0.0009781268,0.000087844,0.00001148685,0.0002836786,0.9970041,0.00005654521,0.0005910228],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.04061673,"threshold_uncertainty_score":0.1358765,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008957622509514486,"score_gpt":0.3489393513177492,"score_spread":0.3399817288082347,"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."}}