{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004387779,0.00136491,0.0007224015,0.001920125,0.0005793149,0.002246915,0.0009973947,0.0009224737,0.0009741942],"category_scores_gemma":[0.007300189,0.0003829565,0.001298666,0.001111805,0.0007259577,0.001558196,0.001005412,0.001951004,0.0009399707],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001029815,"about_ca_system_score_gemma":0.001718972,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003589623,"about_ca_topic_score_gemma":0.005298162,"domain_scores_codex":[0.9983314,0.0006201658,0.0001073012,0.0003933281,0.0004600134,0.00008782315],"domain_scores_gemma":[0.9950899,0.002216664,0.0006129702,0.0005113875,0.001420108,0.0001489445],"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":[0.000814554,0.002155991,0.04525723,0.001057417,0.0005927115,0.0003231361,0.0005176194,0.2116027,0.2086483,0.003331172,0.007875558,0.5178236],"study_design_scores_gemma":[0.00003806153,0.0003521723,0.00914261,0.00008491342,0.0001158624,0.0001500143,0.0001398383,0.8660069,0.1102889,0.006021901,0.007577629,0.00008133998],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3669476,0.004284616,0.6138765,0.002616123,0.0002668122,0.0005087891,0.001954766,0.00456736,0.004977466],"genre_scores_gemma":[0.5282195,0.001804599,0.4642671,0.0007451552,0.0001848648,0.0002568113,0.002661684,0.0002362831,0.001624026],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004387779,"threshold_uncertainty_score":0.0232051,"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."}}