{"id":"W2466137541","doi":"10.1021/acs.analchem.6b01523","title":"Determination of Flow Rates in Capillary Liquid Chromatography Coupled to a Nanoelectrospray Source using Droplet Image Analysis Software","year":2016,"lang":"en","type":"article","venue":"Analytical Chemistry","topic":"Mass Spectrometry Techniques and Applications","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Foundation for Innovation; Nova Scotia Research Innovation Trust","keywords":"Chemistry; Chromatography; Volumetric flow rate; Electrospray; Capillary action; Electrospray ionization; Liquid chromatography–mass spectrometry; Mass spectrometry; Analytical Chemistry (journal); Flow (mathematics); Tandem mass spectrometry; Mechanics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.002985377,0.00107956,0.0007042473,0.001631258,0.0004251332,0.001118501,0.001350456,0.0008841535,0.001587319],"category_scores_gemma":[0.004609196,0.0005396365,0.0003835846,0.0007794979,0.0005218546,0.001314147,0.0005463212,0.001146301,0.0008834419],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009699783,"about_ca_system_score_gemma":0.0007255269,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001035972,"about_ca_topic_score_gemma":0.001208226,"domain_scores_codex":[0.9973493,0.0002517946,0.0002028033,0.000830568,0.00124697,0.000118549],"domain_scores_gemma":[0.997716,0.0008438161,0.000379094,0.0002177766,0.0007434641,0.00009998837],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001343666,0.00005905395,0.0008303252,0.00006956007,0.00001120166,0.00002967222,0.00007359154,0.0002376214,0.980168,0.0002988905,0.0002225767,0.01786516],"study_design_scores_gemma":[0.00001155696,0.0001114804,0.0009345278,0.000008179881,0.00001339723,0.00005304874,0.00001485266,0.007055303,0.9902082,0.0001129894,0.001458091,0.00001843657],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4697905,0.002931827,0.512439,0.0003513616,0.0003974142,0.001349806,0.002065126,0.006139478,0.004535627],"genre_scores_gemma":[0.3966198,0.002705145,0.5914308,0.0003238037,0.00008682821,0.00196201,0.001313241,0.0009689495,0.004589458],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002985377,"threshold_uncertainty_score":0.01578838,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007994222248286937,"score_gpt":0.2673870770322643,"score_spread":0.2593928547839773,"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."}}