{"id":"W4252413300","doi":"10.1515/iupac.88.0231","title":"Chelation Solvent Extraction","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); Solvent extraction; Computer science; Process engineering; Chelation; Throughput; Scale (ratio); Solvent; Sample preparation; Sample (material); Biochemical engineering; Chromatography; Chemistry; Engineering; Organic chemistry; 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":[0.003086681,0.002219422,0.002269588,0.004479702,0.001054146,0.002723147,0.00291207,0.001701527,0.0459767],"category_scores_gemma":[0.01100264,0.0005925501,0.00185659,0.00689267,0.000453058,0.001710624,0.002396391,0.001988591,0.05470977],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001610474,"about_ca_system_score_gemma":0.004167748,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008818045,"about_ca_topic_score_gemma":0.01847437,"domain_scores_codex":[0.9968045,0.0006249403,0.0005651914,0.001106168,0.0006235778,0.0002756825],"domain_scores_gemma":[0.9956248,0.001476462,0.0007023095,0.000867277,0.001145898,0.0001832128],"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":[0.0006650804,0.00007285351,0.005366418,0.01463735,0.0004067296,0.00009019427,0.00007475656,0.000930933,0.002086734,0.00208373,0.9411228,0.03246251],"study_design_scores_gemma":[0.0003158048,0.00004815842,0.005491981,0.001229116,0.000181894,0.0001022418,0.00005613733,0.0002955918,0.001542218,0.002301591,0.9883956,0.00003972609],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0003183927,0.0006184065,0.0005471729,0.00009817455,0.0000384224,0.00007040873,0.9965976,0.0004486134,0.001262808],"genre_scores_gemma":[0.0006378672,0.0005825554,0.001630082,0.00013112,0.00001195915,0.0003176932,0.9957063,0.00009851556,0.0008839505],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.0459767,"threshold_uncertainty_score":0.1538074,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03442927559769279,"score_gpt":0.465160808046659,"score_spread":0.4307315324489662,"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."}}