{"id":"W4234096899","doi":"10.1515/iupac.88.0351","title":"In-Syringe Microextraction","year":2017,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"Digital Imaging in Medicine","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; National Research Council Canada","funders":"","keywords":"Computer science; Extraction (chemistry); Syringe; Process engineering; Chromatography; Sample preparation; Chemistry; Engineering; Mechanical 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0006560094,0.0004729978,0.00104219,0.0005392917,0.00007744117,0.0001068358,0.0003780113,0.0003854047,0.001216505],"category_scores_gemma":[0.002186527,0.0004226848,0.0001678062,0.0001462487,0.0002823091,0.0003038853,0.0001480437,0.001397968,0.00001742073],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007951998,"about_ca_system_score_gemma":0.0009454594,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004094583,"about_ca_topic_score_gemma":0.0006192986,"domain_scores_codex":[0.9970611,0.00003080866,0.0005973705,0.0006019253,0.00121357,0.0004951756],"domain_scores_gemma":[0.997337,0.0000662648,0.0003965434,0.001527475,0.0004222261,0.0002504954],"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":[0.0003449325,0.0005793212,0.0001677528,0.0005930946,0.00008233829,0.002248433,0.00001157941,3.8439e-7,0.00007757622,8.633323e-7,0.9922866,0.003607074],"study_design_scores_gemma":[0.002606764,0.0003436484,0.001210953,0.002426654,0.0002714983,0.0005008424,0.00002093172,0.000006151608,0.00004967017,0.00004435724,0.9921866,0.0003319427],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0007242683,0.001073924,0.00001484759,0.00327287,0.001903142,0.0004987282,0.991521,0.00006301928,0.0009281915],"genre_scores_gemma":[0.0001396751,0.0008218915,0.0001199225,0.001393928,0.001807419,0.00001504601,0.9924143,0.00006308426,0.003224748],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.003275132,"threshold_uncertainty_score":0.9998225,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02248349627989896,"score_gpt":0.4752644227861163,"score_spread":0.4527809265062174,"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."}}