{"id":"W4244649065","doi":"10.1515/iupac.88.0215","title":"Extractive Distillation","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":"Computer science; Process engineering; Extraction (chemistry); Distillation; Sample (material); Scale (ratio); Extractive distillation; Chromatography; Engineering; Chemistry","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":[0.00240737,0.002818084,0.001883833,0.004684425,0.0009964663,0.002986874,0.00296989,0.00166721,0.04046604],"category_scores_gemma":[0.008855487,0.0006550146,0.002214981,0.007531282,0.0005075035,0.0017747,0.002393492,0.002297099,0.05476214],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001530783,"about_ca_system_score_gemma":0.003934065,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01237086,"about_ca_topic_score_gemma":0.02384127,"domain_scores_codex":[0.9966677,0.000648007,0.0004876425,0.001213872,0.0006822613,0.0003006041],"domain_scores_gemma":[0.9965215,0.001236332,0.0005543068,0.0007519616,0.0007968085,0.0001391695],"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":[0.0007973667,0.0000993631,0.006668506,0.0147254,0.0005155783,0.00008229371,0.00008418333,0.001636172,0.001805529,0.003405214,0.9263145,0.04386587],"study_design_scores_gemma":[0.0002387401,0.00005339641,0.005608411,0.00100269,0.0001663521,0.00007907306,0.00005853106,0.0006060588,0.001853278,0.00292545,0.987362,0.00004602496],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0003735469,0.0006829706,0.0005313014,0.00008117259,0.00003673801,0.00004331485,0.9965062,0.0005123103,0.001232342],"genre_scores_gemma":[0.0008525698,0.0006076515,0.001524989,0.00009488912,0.00001098016,0.0001806487,0.995836,0.0001040157,0.0007882147],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.04046604,"threshold_uncertainty_score":0.1353724,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03119394493024112,"score_gpt":0.4614082569209275,"score_spread":0.4302143119906864,"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."}}