{"id":"W4254371880","doi":"10.1515/iupac.88.0242","title":"Liquid–Liquid Distribution","year":2017,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"Electrohydrodynamics and Fluid Dynamics","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; National Research Council Canada","funders":"","keywords":"Computer science; Extraction (chemistry); Process engineering; Sample (material); Scale (ratio); Sample preparation; Throughput; Microwave; 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":[0.002107118,0.002977923,0.001965581,0.004514989,0.000914422,0.003039387,0.003525681,0.002678385,0.04739426],"category_scores_gemma":[0.009616493,0.0006370152,0.00241064,0.006321594,0.0005365177,0.002135911,0.002639248,0.00194593,0.08226422],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00184258,"about_ca_system_score_gemma":0.003230112,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01695747,"about_ca_topic_score_gemma":0.02591013,"domain_scores_codex":[0.9972456,0.0003790835,0.0003832319,0.001112275,0.0006082342,0.0002716529],"domain_scores_gemma":[0.9963837,0.00106152,0.0004973652,0.001035162,0.0008606963,0.000161614],"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":[0.0004431807,0.00008791601,0.006191413,0.00393052,0.000255893,0.00008859271,0.00003659394,0.00178504,0.0006785856,0.001870045,0.9561725,0.02845982],"study_design_scores_gemma":[0.0002986573,0.00005232311,0.006376556,0.0009350837,0.0001156281,0.0001186076,0.00006089579,0.001899772,0.001315512,0.003672602,0.9850957,0.00005863522],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.000328906,0.0004013214,0.0005975057,0.00009488395,0.00005646711,0.00003879914,0.9964582,0.0009482728,0.001075575],"genre_scores_gemma":[0.001014822,0.0002602919,0.000967464,0.00008154897,0.00001667579,0.0001444303,0.9966125,0.0001003835,0.0008019083],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.04739426,"threshold_uncertainty_score":0.1585495,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006339174509339299,"score_gpt":0.3318611089671835,"score_spread":0.3255219344578442,"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."}}