{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003534246,0.0006018154,0.0006783442,0.0001366252,0.000225686,0.0001547716,0.0007799052,0.0006741696,0.0002190106],"category_scores_gemma":[0.0001931716,0.0006251073,0.0002274714,0.0001284397,0.0001117785,0.000131602,0.0001500127,0.0009936979,0.000005757495],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009800614,"about_ca_system_score_gemma":0.0003036604,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007877936,"about_ca_topic_score_gemma":0.001391654,"domain_scores_codex":[0.9976582,0.0000258924,0.0004890501,0.0004307072,0.0007299925,0.000666138],"domain_scores_gemma":[0.9979575,0.00003095819,0.0001529493,0.001387108,0.000272469,0.0001989781],"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":[0.000150975,0.00006307782,3.719169e-7,0.0002164512,0.0001381258,0.00006461549,0.000003168646,0.001047383,0.0001534718,0.00004058468,0.9978447,0.0002770761],"study_design_scores_gemma":[0.0003671764,0.0004613434,0.000006010432,0.0001841645,0.0001323472,0.00001695625,0.000001732406,0.01484849,0.00003393754,0.00003078981,0.9832618,0.0006552752],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.002087843,0.001291855,0.003866132,0.00009034239,0.001539151,0.0002427738,0.9905148,0.0003027387,0.00006434632],"genre_scores_gemma":[0.0007794762,0.003990614,0.0000251733,0.00003264168,0.0008243477,0.00001824032,0.9941381,0.00009507247,0.00009632536],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01458292,"threshold_uncertainty_score":0.99962,"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."}}