{"id":"W2009718682","doi":"10.1021/ac0621120","title":"Selective and Quantitative Detection of Influenza Virus Proteins in Commercial Vaccines Using Two-Dimensional High-Performance Liquid Chromatography and Fluorescence Detection","year":2007,"lang":"en","type":"article","venue":"Analytical Chemistry","topic":"Influenza Virus Research Studies","field":"Medicine","cited_by":65,"is_retracted":false,"has_abstract":true,"ca_institutions":"Health Canada","funders":"Health Canada","keywords":"Chromatography; Chemistry; High-performance liquid chromatography; Hemagglutinin (influenza); Detection limit; Influenza vaccine; Quantitative analysis (chemistry); Antigen; Virus; Reversed-phase chromatography; Influenza A virus; Fluorescence; Virology; Biochemistry; Biology","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":[0.001314072,0.001258814,0.0005572187,0.001044389,0.0002868439,0.000840255,0.0008673369,0.001357425,0.0003686092],"category_scores_gemma":[0.001255068,0.0004770046,0.0006281021,0.000551034,0.0005617823,0.000933348,0.0006389947,0.0008839328,0.0005458627],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005311288,"about_ca_system_score_gemma":0.0005260382,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005993173,"about_ca_topic_score_gemma":0.001086968,"domain_scores_codex":[0.9982474,0.0004922046,0.0001183893,0.0004236902,0.0005992918,0.0001190635],"domain_scores_gemma":[0.9992254,0.000336351,0.000156957,0.00006696195,0.0001780034,0.00003633804],"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":[0.00006101159,0.00008878428,0.0006651497,0.0001548173,0.00002611426,0.00003907874,0.00002873689,0.0001975635,0.986108,0.00009147968,0.0000808128,0.01245846],"study_design_scores_gemma":[0.00001660048,0.0003116156,0.002214039,0.00001039098,0.00003402071,0.0003871209,0.00001838533,0.005038695,0.9902515,0.00009480971,0.001588183,0.00003458445],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4936168,0.01602236,0.4850283,0.0004246842,0.0002377048,0.0006141738,0.0009658487,0.001082341,0.002007785],"genre_scores_gemma":[0.4377103,0.006469669,0.5500877,0.0007561649,0.0001389705,0.001134839,0.001556517,0.00009398893,0.002051925],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001357425,"threshold_uncertainty_score":0.006949544,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0405969549030837,"score_gpt":0.3555890850472423,"score_spread":0.3149921301441586,"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."}}