{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000406025,0.0001689036,0.0003451646,0.0001820921,0.0001217877,0.000009037638,0.0000385493,0.0001203352,0.000006444116],"category_scores_gemma":[0.0004330504,0.0001533286,0.00003923588,0.0006185081,0.00033852,0.0001294847,0.00007967286,0.000369748,7.250204e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000108545,"about_ca_system_score_gemma":0.00005747739,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000647353,"about_ca_topic_score_gemma":0.0001643593,"domain_scores_codex":[0.9987049,0.00002607697,0.0003683795,0.0002869421,0.000308219,0.0003055231],"domain_scores_gemma":[0.9992188,0.0001699428,0.00009477004,0.0001162878,0.0002805293,0.00011968],"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":[0.003047936,0.00009535316,0.03650701,0.0003399466,0.00009058855,0.00001114652,0.000133701,0.00004774202,0.9583949,0.000006841262,7.528445e-7,0.001324014],"study_design_scores_gemma":[0.001279746,0.0004098012,0.204079,0.0001707161,0.00005073323,0.00002717706,0.0000737974,0.008577006,0.7851979,0.00001792022,0.000003828034,0.0001124211],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9990932,0.0003364234,0.0001555956,0.00001707839,0.00001196166,0.0002654383,0.000004720669,0.00002047481,0.00009507849],"genre_scores_gemma":[0.9991608,0.00003628126,0.0006686096,0.00005099676,0.00005643619,0.000009743948,0.000001414055,0.00001242201,0.000003284062],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1731971,"threshold_uncertainty_score":0.6252559,"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."}}