{"id":"W2086022505","doi":"10.5210/ojphi.v5i1.4574","title":"Bayesian Contact Tracing for Communicable Respiratory Disease","year":2013,"lang":"en","type":"article","venue":"Online Journal of Public Health Informatics","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Contact tracing; Computer science; Communicable disease; Bayesian probability; Population; Probabilistic logic; Dynamic Bayesian network; Pandemic; Tracing; Public health; Data science; Artificial intelligence; Data mining; Machine learning; Coronavirus disease 2019 (COVID-19); Medicine; Disease; Infectious disease (medical specialty); Environmental health","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003038405,0.0001778387,0.0007124758,0.00032947,0.0001639761,0.0001075146,0.0003629049,0.00006439289,0.0001604001],"category_scores_gemma":[0.00236414,0.0001414115,0.0002164955,0.0002424262,0.00005616522,0.001318632,0.0000665316,0.0004841768,0.00002173823],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003261967,"about_ca_system_score_gemma":0.003541291,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001925189,"about_ca_topic_score_gemma":0.00001360499,"domain_scores_codex":[0.9963667,0.0001642394,0.002314989,0.00006379611,0.0005244713,0.0005657423],"domain_scores_gemma":[0.994394,0.0002762146,0.00166193,0.0005946871,0.001129035,0.0019441],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005664437,0.003355897,0.08345465,0.009897009,0.00064419,0.00004295142,0.004017601,0.0002165221,0.00005187429,0.001219992,0.3957726,0.5007603],"study_design_scores_gemma":[0.00651664,0.00174082,0.1110439,0.001094218,0.00007049761,0.0001156404,0.003112076,0.03356676,0.000004176697,0.0002208706,0.8422309,0.0002834786],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6338,0.006981835,0.212526,0.1336273,0.001390896,0.005567732,0.002653998,0.0003022024,0.003150143],"genre_scores_gemma":[0.8280835,0.0007465206,0.1224922,0.04679266,0.0008831908,0.00003999659,0.0006605464,0.00007667295,0.0002247278],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5004768,"threshold_uncertainty_score":0.6282099,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09570321424380361,"score_gpt":0.3745769563889753,"score_spread":0.2788737421451717,"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."}}