{"id":"W4249760112","doi":"10.1515/iupac.88.0211","title":"Closed-Loop Extraction","year":2017,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"Microfluidic and Capillary Electrophoresis Applications","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; National Research Council Canada","funders":"","keywords":"Extraction (chemistry); Computer science; Closed loop; Process engineering; Sample (material); Scale (ratio); Microwave; Sample preparation; Throughput; Chromatography; Control engineering; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001903278,0.0003936647,0.0004358806,0.0001438994,0.0002320462,0.0001147608,0.000524799,0.0004408599,0.001606237],"category_scores_gemma":[0.00006100168,0.0004021702,0.0001467369,0.0000925876,0.00007359446,0.00008693999,0.00005067842,0.0006824899,0.00002073082],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00036517,"about_ca_system_score_gemma":0.0002383336,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007035659,"about_ca_topic_score_gemma":0.0001514697,"domain_scores_codex":[0.9984074,0.00001952935,0.0003487837,0.0003375971,0.0005086517,0.000378095],"domain_scores_gemma":[0.9982983,0.00002574142,0.00012971,0.001245573,0.0001687187,0.00013196],"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":[0.00001460607,0.00005433832,6.536342e-7,0.0001271428,0.00009824207,0.00001959507,0.00000304318,0.000005144937,0.001952309,0.000008837941,0.9947734,0.002942726],"study_design_scores_gemma":[0.000239736,0.00004327905,0.00003390676,0.00007647424,0.0001294325,0.00002014962,0.000006283963,0.00002355709,0.0007956934,0.00005288107,0.9981747,0.0004039133],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0002896472,0.03516143,0.0003036842,0.00007016735,0.0006156926,0.0002550455,0.9628513,0.000217603,0.0002354097],"genre_scores_gemma":[0.00004390952,0.1692693,0.0000102259,0.00003805735,0.0007191044,0.00003661312,0.8293991,0.00005536951,0.000428327],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1341078,"threshold_uncertainty_score":0.999843,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01068930531395118,"score_gpt":0.3645382242247956,"score_spread":0.3538489189108444,"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."}}