{"id":"W4251489365","doi":"10.1515/iupac.88.0339","title":"Ultrafiltration","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":"Ultrafiltration (renal); Computer science; Extraction (chemistry); Process engineering; Throughput; Sample (material); Sample preparation; Scale (ratio); Chromatography; Biochemical engineering; 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":[0.002933074,0.002323243,0.00222317,0.004083881,0.0009645806,0.003246098,0.002859307,0.001558812,0.04112649],"category_scores_gemma":[0.00940085,0.000546595,0.001832961,0.00819183,0.0004196467,0.001863799,0.002325291,0.002087551,0.04841109],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001688665,"about_ca_system_score_gemma":0.004168649,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01008587,"about_ca_topic_score_gemma":0.01553447,"domain_scores_codex":[0.9967866,0.0005942561,0.0004936157,0.001190554,0.0006275771,0.0003075156],"domain_scores_gemma":[0.9963103,0.00118843,0.0006645013,0.0007700955,0.000870498,0.0001961743],"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":[0.001130516,0.00009327978,0.007257891,0.01482982,0.0006084009,0.00009731491,0.00008914496,0.001116253,0.002079189,0.002853127,0.9305624,0.03928254],"study_design_scores_gemma":[0.0004574236,0.00007898541,0.01002873,0.001262162,0.0002258487,0.0001176857,0.00006493074,0.0004672159,0.001758529,0.002946158,0.9825434,0.00004891788],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0004066246,0.0005760053,0.0005249387,0.00006538989,0.00003180837,0.00005522489,0.9968934,0.0004408124,0.001005846],"genre_scores_gemma":[0.0007602103,0.0005214228,0.001201115,0.00008604795,0.000009512071,0.000235917,0.9964809,0.000093462,0.0006113998],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.04112649,"threshold_uncertainty_score":0.1375818,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02926668380349771,"score_gpt":0.4459447711690682,"score_spread":0.4166780873655704,"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."}}