{"id":"W4255813044","doi":"10.1515/iupac.88.0358","title":"Microfluidic Extraction Device","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":"Extraction (chemistry); Computer science; Microfluidics; Sample (material); Process engineering; Throughput; Scale (ratio); Sample preparation; Nanotechnology; Chromatography; Engineering; Materials science; Chemistry; Telecommunications; 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":[0.001794359,0.002073423,0.001788175,0.003326229,0.0008575595,0.002472972,0.002567529,0.001600196,0.05647192],"category_scores_gemma":[0.008223472,0.0006174957,0.001379583,0.005963033,0.000366737,0.001505858,0.001904458,0.001842064,0.06947849],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001321367,"about_ca_system_score_gemma":0.003693895,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009401721,"about_ca_topic_score_gemma":0.01921871,"domain_scores_codex":[0.9978769,0.0003717578,0.0003236875,0.0007007089,0.0005178914,0.0002089949],"domain_scores_gemma":[0.9967592,0.001255396,0.0005152036,0.0005700646,0.0007398996,0.0001602131],"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":[0.0004885556,0.000066512,0.004550963,0.007534426,0.0001952822,0.00006831538,0.00004256589,0.0008545675,0.00106427,0.001616312,0.9564129,0.02710538],"study_design_scores_gemma":[0.0002101248,0.00004640803,0.005658897,0.0008614513,0.0001080786,0.0001001499,0.00004695261,0.0003426406,0.001288021,0.001987337,0.9893143,0.00003554388],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0002531215,0.000410241,0.0004083541,0.00006154877,0.00002877682,0.00004761685,0.9970689,0.0004457515,0.001275674],"genre_scores_gemma":[0.0006271838,0.0004687346,0.001039163,0.0001051158,0.00001018453,0.0002291464,0.996645,0.0000694761,0.0008060492],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.05647192,"threshold_uncertainty_score":0.1889174,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0351418947524849,"score_gpt":0.4601920967587654,"score_spread":0.4250502020062805,"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."}}