{"id":"W2936924363","doi":"10.1101/609248","title":"Predicting the short-term success of human influenza A variants with machine learning","year":2019,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Influenza Virus Research Studies","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Engineering and Physical Sciences Research Council; Natural Sciences and Engineering Research Council of Canada","keywords":"Hemagglutinin (influenza); Biology; Seasonal influenza; Human influenza; Influenza A virus; Virology; Phylogenetic tree; Virus; Computational biology; Term (time); Classifier (UML); Artificial intelligence; Computer science; Machine learning; Gene; Genetics; Disease; Coronavirus disease 2019 (COVID-19); Infectious disease (medical specialty); Medicine","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":[0.001916054,0.0005945633,0.0004204702,0.001969189,0.0002574972,0.0006577308,0.0003562027,0.0008580257,0.0007718832],"category_scores_gemma":[0.006214433,0.0001939151,0.0004724937,0.001035161,0.0002751692,0.0008426753,0.0004162714,0.0006583451,0.0006879295],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004670119,"about_ca_system_score_gemma":0.0002936583,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00316026,"about_ca_topic_score_gemma":0.002841742,"domain_scores_codex":[0.9994735,0.0002202635,0.00003657942,0.0001237211,0.00008926829,0.00005664798],"domain_scores_gemma":[0.9951959,0.00337584,0.0005039452,0.0002997442,0.0004001205,0.0002243748],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004799273,0.0005625564,0.5139396,0.00007666847,0.0002879902,0.0001208414,0.00008580788,0.3277119,0.004993056,0.0004113067,0.002874403,0.1484558],"study_design_scores_gemma":[0.00000453801,0.00008748934,0.03597106,0.0000100401,0.00001276117,0.00004214929,0.00003490349,0.9612657,0.001397924,0.0009797149,0.0001846853,0.000009142447],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9659132,0.000581769,0.03031953,0.0004253843,0.00004541625,0.00003338026,0.0009319077,0.000464247,0.001285283],"genre_scores_gemma":[0.989509,0.00006986513,0.009171914,0.00003192059,0.00003303278,0.00001267359,0.0008851424,0.000009837133,0.0002765658],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00316026,"threshold_uncertainty_score":0.01013315,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04543910150572627,"score_gpt":0.3138777014198779,"score_spread":0.2684385999141516,"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."}}