{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003697798,0.002491395,0.002212611,0.003406244,0.0009291184,0.003388207,0.003549538,0.002408653,0.03878628],"category_scores_gemma":[0.01333581,0.0006852885,0.002160891,0.005245264,0.0005406152,0.001692374,0.002751089,0.002112102,0.05959401],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001543134,"about_ca_system_score_gemma":0.00397841,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008276085,"about_ca_topic_score_gemma":0.01582636,"domain_scores_codex":[0.9956156,0.0009598579,0.0008173702,0.001419395,0.0008894301,0.0002982757],"domain_scores_gemma":[0.994229,0.002144959,0.0009137793,0.001299012,0.001216492,0.0001967314],"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":[0.001090162,0.0001160049,0.008421686,0.012167,0.0006092425,0.0001366484,0.00007989365,0.001247147,0.002190949,0.002008719,0.9242919,0.0476406],"study_design_scores_gemma":[0.0004975909,0.00009595665,0.009821465,0.001418831,0.0002748178,0.0003147156,0.00007660611,0.001100908,0.003226441,0.004320763,0.9787763,0.00007561895],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0007882268,0.001551835,0.002134112,0.00020623,0.0001075051,0.0001669074,0.9915531,0.001497559,0.001994393],"genre_scores_gemma":[0.001521582,0.0009943561,0.00384916,0.000301875,0.00003460682,0.0006024967,0.9911076,0.0002052242,0.00138297],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.03878628,"threshold_uncertainty_score":0.129753,"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."}}