{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005563746,0.000553433,0.0007182335,0.00005825999,0.0002378909,0.0001812251,0.001005483,0.0007873538,0.02223971],"category_scores_gemma":[0.002837079,0.0005281733,0.0002261939,0.00005705883,0.0002194773,0.00008413751,0.0001857405,0.001020367,0.000008732425],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007030347,"about_ca_system_score_gemma":0.001208825,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005714567,"about_ca_topic_score_gemma":0.0001173374,"domain_scores_codex":[0.9968695,0.00002700715,0.0005939034,0.0007023153,0.001336794,0.0004704503],"domain_scores_gemma":[0.9969376,0.0001428148,0.0005067232,0.001717262,0.000422795,0.0002728524],"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":[0.00006017927,0.0001438106,0.000008426978,0.0005849768,0.000150123,0.0000984096,0.000004678313,6.938548e-7,0.0006433515,0.000001593174,0.9964197,0.001884099],"study_design_scores_gemma":[0.0004404138,0.00001474699,0.000007322959,0.0003493179,0.0001955112,0.00002499846,0.0000166952,0.00001293713,0.005226552,0.0001213929,0.9930026,0.0005875169],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00002787319,0.0001984726,0.0006610535,0.0002137214,0.0002419431,0.00006335386,0.9964726,0.00009227957,0.002028693],"genre_scores_gemma":[0.00001282372,0.0003426026,0.001576144,0.00008480751,0.001215533,0.00001567376,0.9922464,0.00004494884,0.004460997],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.02223098,"threshold_uncertainty_score":0.999717,"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."}}