{"id":"W2767767149","doi":"10.1021/acs.analchem.7b04329","title":"Advances in Microchip Liquid Chromatography","year":2017,"lang":"en","type":"article","venue":"Analytical Chemistry","topic":"Microfluidic and Capillary Electrophoresis Applications","field":"Engineering","cited_by":94,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Chemistry; Chromatography; Hydrophilic interaction chromatography; High-performance liquid chromatography","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.003273085,0.001835865,0.001428633,0.003517791,0.001078714,0.004620777,0.002712359,0.002495323,0.07232395],"category_scores_gemma":[0.004054627,0.001154073,0.001242101,0.002659266,0.001362341,0.004470217,0.003250228,0.006042506,0.08796258],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002276876,"about_ca_system_score_gemma":0.002139838,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001955743,"about_ca_topic_score_gemma":0.002656151,"domain_scores_codex":[0.9944524,0.0006602371,0.0002782214,0.0009053628,0.00326591,0.0004379096],"domain_scores_gemma":[0.9974112,0.0005170865,0.0001773194,0.0004634892,0.001177619,0.0002534018],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001395549,0.0002017279,0.0005917593,0.001522169,0.0001463277,0.0002462048,0.0001901055,0.0004473538,0.03785604,0.03241376,0.2848397,0.6414052],"study_design_scores_gemma":[0.00001957354,0.0001002895,0.0004027066,0.0001910474,0.00002396942,0.0004608523,0.0000374643,0.001380732,0.02380528,0.004903816,0.9686283,0.00004593107],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"review","genre_gemma":"methods","genre_scores_codex":[0.006965877,0.4232641,0.2292773,0.02627155,0.02887412,0.001076597,0.00523601,0.01267918,0.2663552],"genre_scores_gemma":[0.0783594,0.2767749,0.2132714,0.01415494,0.009667928,0.001466387,0.007678223,0.002741444,0.3958854],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.07232395,"threshold_uncertainty_score":0.2419477,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005318983624865255,"score_gpt":0.2290198100114279,"score_spread":0.2237008263865627,"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."}}