{"id":"W4413008761","doi":"10.3390/vaccines13080820","title":"Advancing Reversed-Phase Chromatography Analytics of Influenza Vaccines Using Machine Learning Approaches on a Diverse Range of Antigens and Formulations","year":2025,"lang":"en","type":"article","venue":"Vaccines","topic":"Influenza Virus Research Studies","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Shared Services Canada; Health Canada","funders":"Health Canada","keywords":"Computer science; Machine learning; Artificial intelligence; Influenza vaccine; Scalability; Data mining; Medicine; Immunology; Vaccination","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":[0.000318698,0.0002155421,0.0006267529,0.001014496,0.000211604,0.00001049513,0.00008304304,0.0000768778,0.00002251385],"category_scores_gemma":[0.0008277807,0.0001772164,0.000151381,0.001062604,0.00006894071,0.0001464278,0.0001533445,0.000209284,7.555649e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003740881,"about_ca_system_score_gemma":0.00007767921,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002753281,"about_ca_topic_score_gemma":0.00007540857,"domain_scores_codex":[0.9986302,0.00005920168,0.0005094115,0.000261385,0.0002535869,0.0002861913],"domain_scores_gemma":[0.9988527,0.0002522012,0.0002246241,0.0002846854,0.0003087183,0.00007702374],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001114029,0.0003051893,0.9754845,0.001334607,0.0005766665,0.000009841305,0.0007458937,0.002165027,0.01538949,0.0003792896,0.00009652547,0.00239898],"study_design_scores_gemma":[0.02645287,0.001977519,0.7447276,0.00316981,0.001981809,0.00002814577,0.004244901,0.1701304,0.04144842,0.000413112,0.004734839,0.0006906279],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9953532,0.002947506,0.000510773,0.0001163195,0.00003965079,0.0005051253,0.00003791256,0.00004289708,0.0004466029],"genre_scores_gemma":[0.9958274,0.0005274109,0.003398822,0.00009729591,0.00003846969,0.000007635936,0.000008810139,0.00001917611,0.00007493287],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2307569,"threshold_uncertainty_score":0.7226677,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1180382542668529,"score_gpt":0.3949706591514162,"score_spread":0.2769324048845633,"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."}}