{"id":"W4238308788","doi":"10.1515/iupac.88.0273","title":"Conditioning Solvent","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; Extraction (chemistry); Process engineering; Solvent extraction; Sample (material); Sample preparation; Biochemical engineering; Chromatography; Chemistry; Engineering","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":[0.003022006,0.002176095,0.002061448,0.003989551,0.001111263,0.002761446,0.002677813,0.001855153,0.06956734],"category_scores_gemma":[0.01059969,0.000611785,0.001877466,0.007166754,0.0004891574,0.002177658,0.002046403,0.002122603,0.08091797],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001513497,"about_ca_system_score_gemma":0.004270724,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009652669,"about_ca_topic_score_gemma":0.02059857,"domain_scores_codex":[0.9970534,0.0005370239,0.0005529876,0.001076134,0.0005469482,0.00023344],"domain_scores_gemma":[0.9957592,0.001607971,0.0005854137,0.0008944882,0.0009894688,0.000163422],"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":[0.0009964176,0.0001311457,0.006645268,0.01395148,0.0004278397,0.0000973572,0.00008891329,0.001022331,0.00303163,0.001968998,0.908905,0.06273346],"study_design_scores_gemma":[0.0002534282,0.00005443715,0.005365445,0.0008231218,0.0001574563,0.00009415077,0.00004711875,0.0002897778,0.001975735,0.001770442,0.989131,0.00003798648],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0006723785,0.001058155,0.001103701,0.0001107138,0.00006261805,0.0001420299,0.9935174,0.001032192,0.002300739],"genre_scores_gemma":[0.000962475,0.0007097478,0.002345871,0.0001501973,0.00001529713,0.0004602,0.9940095,0.0001763331,0.001170474],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.06956734,"threshold_uncertainty_score":0.2327259,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0305452067669937,"score_gpt":0.447365500173783,"score_spread":0.4168202934067893,"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."}}