{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002779021,0.0005031177,0.0006341658,0.0001716245,0.0002686117,0.0001186248,0.0006333859,0.0005605464,0.001493248],"category_scores_gemma":[0.00009643145,0.000518706,0.0001916231,0.0001039107,0.000105208,0.0001116112,0.00007401791,0.0007929369,0.0000221025],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004223153,"about_ca_system_score_gemma":0.0002949768,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008166536,"about_ca_topic_score_gemma":0.0001447387,"domain_scores_codex":[0.9979833,0.00003148156,0.0004805779,0.0004282364,0.0005788567,0.0004975496],"domain_scores_gemma":[0.9979345,0.00003544608,0.0001905783,0.001403994,0.0002609352,0.0001745901],"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":[0.0001597581,0.00007467857,1.305751e-7,0.0001346085,0.0001340082,0.00003431112,0.000006668075,0.000005577144,0.008640826,0.00001022408,0.9902816,0.0005176474],"study_design_scores_gemma":[0.0003160188,0.0003270609,0.000003895594,0.0001166717,0.0001652862,0.00004766656,0.0000117493,0.000007282034,0.003583122,0.00001000228,0.9948898,0.0005214679],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.001206325,0.04099654,0.0004913383,0.00009690417,0.0007310456,0.0003545898,0.9555765,0.0002924775,0.0002542456],"genre_scores_gemma":[0.0001943429,0.1511371,0.000009885864,0.00005854648,0.0009162923,0.00005899074,0.8472778,0.00007727952,0.0002698221],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1101405,"threshold_uncertainty_score":0.9997265,"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."}}