{"id":"W4251874266","doi":"10.1515/iupac.88.0204","title":"Sequential 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":"Computer science; Extraction (chemistry); Sample (material); Process engineering; Throughput; Scale (ratio); Chromatography; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003336201,0.002763397,0.002372617,0.004601156,0.001197932,0.002998506,0.003153195,0.001944383,0.07143173],"category_scores_gemma":[0.01407387,0.0007657938,0.002481439,0.007206202,0.0005033075,0.002133128,0.002425666,0.002273108,0.08975421],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001713143,"about_ca_system_score_gemma":0.005137571,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009836606,"about_ca_topic_score_gemma":0.02058098,"domain_scores_codex":[0.9958814,0.0007533982,0.0007505554,0.001492365,0.0007915237,0.0003308318],"domain_scores_gemma":[0.9940451,0.001992663,0.0008104749,0.001304813,0.001633782,0.0002131979],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0008684315,0.00007679399,0.00377582,0.01391316,0.0004401053,0.0000954811,0.00006414799,0.0007814534,0.001753046,0.002147976,0.9372154,0.03886806],"study_design_scores_gemma":[0.0003134465,0.00004821141,0.003586087,0.001251541,0.000180351,0.0001027312,0.00005545655,0.0002744378,0.00123571,0.002555549,0.9903564,0.00004012306],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"methods","genre_scores_codex":[0.0002778786,0.0006084861,0.0007747399,0.00008326078,0.00005371466,0.000111191,0.9959891,0.0006330695,0.001468519],"genre_scores_gemma":[0.000587427,0.0005807342,0.00197749,0.0001401532,0.00001489591,0.0004009772,0.9950587,0.0001329225,0.001106754],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.07143173,"threshold_uncertainty_score":0.2389629,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03972100622653298,"score_gpt":0.4771104860778064,"score_spread":0.4373894798512734,"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."}}