{"id":"W2314403353","doi":"10.1142/9789814667944_0018","title":"A METHOD FOR CLUSTERING HEMAGGLUTININ INFLUENZA PROTEIN SEQUENCES","year":2015,"lang":"en","type":"article","venue":"","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"York University","funders":"","keywords":"Hemagglutinin (influenza); Cluster analysis; Computer science; Computational biology; Artificial intelligence; Virology; Biology; Virus","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0008550321,0.001159833,0.001190939,0.003002647,0.001775297,0.001443682,0.00197687,0.001654373,0.004395016],"category_scores_gemma":[0.002463971,0.000770798,0.001538713,0.003273066,0.0006192172,0.001151679,0.001293542,0.001537634,0.003922572],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005770323,"about_ca_system_score_gemma":0.001863298,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006683601,"about_ca_topic_score_gemma":0.01076926,"domain_scores_codex":[0.9989123,0.0001364882,0.00008902354,0.0002608947,0.0005230043,0.00007816467],"domain_scores_gemma":[0.9989322,0.0002805743,0.00006050987,0.0001941715,0.0004535783,0.00007901825],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003316817,0.000179156,0.001327605,0.0002656751,0.0002203999,0.00013579,0.0001739214,0.01431118,0.05403176,0.005137182,0.0150247,0.908861],"study_design_scores_gemma":[0.0002604509,0.0003228123,0.005252474,0.00007244202,0.0001942363,0.001340015,0.0002948717,0.8243975,0.07144802,0.03106751,0.06517041,0.0001792808],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004957689,0.0002505867,0.9904054,0.00008848486,0.000110449,0.0001442373,0.0003502714,0.003040266,0.0006526002],"genre_scores_gemma":[0.02007382,0.0001717077,0.9742004,0.0000745512,0.00005209368,0.000163351,0.001263737,0.0002720841,0.003728177],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006683601,"threshold_uncertainty_score":0.01470274,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03912513262033836,"score_gpt":0.3557031499078894,"score_spread":0.316578017287551,"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."}}