{"id":"W3011246689","doi":"10.1093/ve/veaa011","title":"Public health in genetic spaces: a statistical framework to optimize cluster-based outbreak detection","year":2020,"lang":"en","type":"article","venue":"Virus Evolution","topic":"HIV Research and Treatment","field":"Immunology and Microbiology","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"National Institute of Allergy and Infectious Diseases; Canadian Institutes of Health Research; Center for AIDS Research, University of Washington","keywords":"Pairwise comparison; Cluster analysis; Covariate; Context (archaeology); Statistics; Population; Statistical power; Cluster (spacecraft); Null hypothesis; Econometrics; Computer science; Geography; Data mining; Mathematics; Demography","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001930708,0.0001290108,0.0002073871,0.0001570872,0.0001343528,0.00002605661,0.0001006505,0.0001565877,0.0003334471],"category_scores_gemma":[0.0004381233,0.0001202799,0.00003771313,0.0003662875,0.00006620779,0.00004535199,0.00004715174,0.0002963089,0.002709346],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004194886,"about_ca_system_score_gemma":0.0002646911,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001198942,"about_ca_topic_score_gemma":0.0008975223,"domain_scores_codex":[0.998401,0.0004535641,0.0002351473,0.0003327772,0.00005590712,0.0005215693],"domain_scores_gemma":[0.9994386,0.0001470574,0.00004773019,0.000161909,0.00003846515,0.0001662645],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.01018702,0.004858213,0.04552493,0.0004611612,0.0007624623,0.0001540356,0.009112981,0.0181136,0.1778635,0.006924281,0.03770105,0.6883368],"study_design_scores_gemma":[0.02523473,0.02074949,0.6680567,0.0003675649,0.00009919976,0.00008881297,0.001294551,0.02519858,0.01750087,0.003231778,0.2361991,0.001978562],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1064746,0.001052079,0.8755457,0.01549943,0.0001904871,0.0007441959,0.0002952542,0.0001075393,0.00009063238],"genre_scores_gemma":[0.989458,0.00002552994,0.00874491,0.001460229,0.00003224797,0.00009997465,0.0001379529,0.0000133836,0.00002779741],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8829833,"threshold_uncertainty_score":0.9980671,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04723515533997124,"score_gpt":0.2941885136552284,"score_spread":0.2469533583152571,"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."}}