{"id":"W4253941137","doi":"10.1515/iupac.88.0343","title":"Automatic 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); Throughput; Process engineering; Chromatography; Engineering; Chemistry; 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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.003048158,0.003692114,0.00240393,0.005543604,0.001417686,0.003213199,0.003714523,0.002553402,0.05429736],"category_scores_gemma":[0.01190672,0.0008094134,0.002581537,0.00706336,0.0006194733,0.002572181,0.003072747,0.002455186,0.1103745],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001913303,"about_ca_system_score_gemma":0.004534625,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01151936,"about_ca_topic_score_gemma":0.02396294,"domain_scores_codex":[0.9961529,0.0006253736,0.0005860251,0.001499289,0.0007627036,0.0003735924],"domain_scores_gemma":[0.99545,0.001296005,0.0005042123,0.001170341,0.00140444,0.0001750267],"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":[0.0004530323,0.00006116321,0.003070565,0.006290313,0.0002098836,0.00007965722,0.00005025518,0.0006615001,0.00125076,0.001590147,0.9547462,0.03153643],"study_design_scores_gemma":[0.0002051236,0.00003568895,0.003499981,0.0009483012,0.0001091131,0.0001021173,0.00006792405,0.0005090351,0.001361465,0.002927417,0.9901894,0.00004453931],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0002525835,0.0005601464,0.0008440389,0.0001071193,0.00006946729,0.00008036374,0.9954595,0.001197909,0.001428756],"genre_scores_gemma":[0.0004581674,0.000345641,0.002032836,0.0001088759,0.00001314001,0.000250247,0.9959115,0.0001097504,0.0007698063],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.9457027,"threshold_uncertainty_score":0.1816428,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03071477398867093,"score_gpt":0.4667430702901016,"score_spread":0.4360282963014306,"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."}}