{"id":"W3034189653","doi":"10.1101/2020.06.12.145151","title":"Integrating genotypes and phenotypes improves long-term forecasts of seasonal influenza A/H3N2 evolution","year":2020,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Influenza Virus Research Studies","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Institute of Infection and Immunity; National Institute of General Medical Sciences; National Institute of Allergy and Infectious Diseases; Medical Research Council; Japan Agency for Medical Research and Development; Centers for Disease Control and Prevention; Ministry of Health, Labour and Welfare; Australian Government; Wellcome Trust; Francis Crick Institute; National Institutes of Health; Cancer Research UK","keywords":"Antigenic drift; Hemagglutinin (influenza); Constraint (computer-aided design); Biology; Population; Phenotype; Influenza vaccine; Sequence (biology); Immune escape; Selection (genetic algorithm); Vaccination; Evolutionary biology; Virology; Genetics; Antigen; Gene; Computer science; Immune system; Demography; Mathematics; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001701185,0.000740507,0.0004552357,0.0003514795,0.0002553582,0.000789193,0.0004472325,0.0008634613,0.0006140177],"category_scores_gemma":[0.003901347,0.0003230812,0.0005168227,0.0002449287,0.0002191412,0.0009624748,0.0005093808,0.000808607,0.0001982609],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000625626,"about_ca_system_score_gemma":0.0007642898,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02270292,"about_ca_topic_score_gemma":0.01498583,"domain_scores_codex":[0.9997765,0.00008397702,0.00001354003,0.00007856949,0.00002378008,0.00002344389],"domain_scores_gemma":[0.9990175,0.0005759042,0.0001144599,0.0000762245,0.0001291311,0.00008684713],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007830753,0.00004478864,0.008819219,0.000009697378,0.00004695596,0.00001582129,0.00001513149,0.9789591,0.001619495,0.0002220708,0.000317751,0.009851689],"study_design_scores_gemma":[0.000003398466,0.00001155821,0.001244196,0.00000141799,0.00000454948,0.000002013043,0.000002841394,0.9983073,0.0001624848,0.0002039168,0.00005215451,0.000004107253],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9101313,0.0004000077,0.085076,0.0009224438,0.0001051223,0.00002213019,0.000644603,0.0006959771,0.002002374],"genre_scores_gemma":[0.9917555,0.00004503628,0.007478045,0.0000550796,0.00002491042,0.00000625789,0.0002820117,0.00002421032,0.0003288754],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02270292,"threshold_uncertainty_score":0.04514158,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04098246526319958,"score_gpt":0.3009190810630904,"score_spread":0.2599366157998908,"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."}}