{"id":"W2473954818","doi":"10.1016/j.socscimed.2016.06.047","title":"Identification of spatial and cohort clustering of tuberculosis using surveillance data from British Columbia, Canada, 1990–2013","year":2016,"lang":"en","type":"article","venue":"Social Science & Medicine","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":18,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia; BC Centre for Disease Control","funders":"British Columbia Centre for Disease Control; Public Health Agency of Canada","keywords":"Incidence (geometry); Demography; Cohort; Geography; Population; Cluster analysis; Census; Cohort effect; Cohort study; Cartography; Medicine; Statistics; Pathology; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001408438,0.0004075117,0.000463565,0.00315649,0.001445235,0.001403679,0.001663504,0.0005002627,0.001050099],"category_scores_gemma":[0.005075279,0.000446546,0.0007274571,0.008314776,0.0005570868,0.000469759,0.001290744,0.0008360114,0.0002651072],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02370286,"about_ca_system_score_gemma":0.04138774,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.997816,"about_ca_topic_score_gemma":0.9987137,"domain_scores_codex":[0.9989309,0.00009847237,0.0001234254,0.0002441086,0.0002828748,0.0003202959],"domain_scores_gemma":[0.9946567,0.0003123458,0.0008163532,0.000312818,0.003243925,0.0006579481],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00006752646,0.00001514344,0.9886929,0.00005899904,0.0001542354,0.00004643279,0.0004840279,0.0007712555,0.0001611536,0.0001817177,0.003755504,0.005610966],"study_design_scores_gemma":[0.000005279589,0.000005965329,0.9956963,0.00005922777,0.00005508077,0.00003096752,0.0008992422,0.001435863,0.0000844606,0.00004400637,0.001672201,0.00001143625],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9548084,0.002102976,0.0007325612,0.0007852394,0.0000353993,0.0000671548,0.03927688,0.00005549063,0.002135858],"genre_scores_gemma":[0.9854633,0.0007508445,0.0007440368,0.0000946271,0.000007610207,0.00003377188,0.01175283,0.00001151283,0.001141524],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02370286,"threshold_uncertainty_score":0.1719771,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02116744135129956,"score_gpt":0.287063965682525,"score_spread":0.2658965243312255,"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."}}