{"id":"W4236693879","doi":"10.1515/iupac.88.0181","title":"Extractant","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":"Extraction (chemistry); Computer science; Process engineering; Sample (material); Sample preparation; Throughput; Scale (ratio); Biochemical engineering; Chromatography; Chemistry; Engineering; Physics; Telecommunications","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":[0.002002301,0.002612349,0.002017505,0.004389472,0.001017395,0.002458296,0.002822496,0.002106706,0.0664221],"category_scores_gemma":[0.009692434,0.0007025968,0.001916411,0.006391478,0.0004430083,0.001765765,0.002103865,0.002171714,0.09068601],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001536646,"about_ca_system_score_gemma":0.003686874,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01109055,"about_ca_topic_score_gemma":0.02222645,"domain_scores_codex":[0.9976003,0.0003907426,0.0004148653,0.0008196348,0.0005566109,0.0002178464],"domain_scores_gemma":[0.9965277,0.00117789,0.0005661456,0.0006242777,0.0009545471,0.0001494257],"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":[0.0007484917,0.00008289759,0.004037596,0.01057915,0.0002423197,0.00008238247,0.00006567777,0.0007348912,0.001538459,0.001662058,0.95788,0.02234619],"study_design_scores_gemma":[0.0002623444,0.00004376929,0.004272145,0.0009482715,0.0001157549,0.00007825709,0.00005197995,0.0002300038,0.001026122,0.001325676,0.9916146,0.0000311593],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.000207969,0.0003262675,0.0002160989,0.00005901457,0.00002313017,0.00004065473,0.9979842,0.0003187256,0.0008240136],"genre_scores_gemma":[0.0004009367,0.0003169407,0.0007775423,0.00009322671,0.000007216114,0.0001832707,0.9975096,0.0000726942,0.0006384605],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.0664221,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03254782188809548,"score_gpt":0.4573930996175677,"score_spread":0.4248452777294722,"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."}}