{"id":"W3181216097","doi":"10.1038/s41597-021-00955-2","title":"A sub-national real-time epidemiological and vaccination database for the COVID-19 pandemic in Canada","year":2021,"lang":"en","type":"article","venue":"Scientific Data","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph; SickKids Foundation; Hospital for Sick Children; Public Health Ontario; University of Toronto","funders":"Medical Research Council; National Institute for Health and Care Research; Wellcome Trust","keywords":"Pandemic; Coronavirus disease 2019 (COVID-19); Dashboard; Public health; Epidemiology; Outbreak; Vaccination; Open data; Geography; Political science; Medicine; Database; Computer science; World Wide Web; Nursing; Virology; Infectious disease (medical specialty)","routes":{"ca_aff":true,"ca_fund":false,"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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.004105535,0.00009117717,0.0002006614,0.00005433651,0.0001767106,0.00005556714,0.0004019243,0.0000277118,0.0003239176],"category_scores_gemma":[0.01734181,0.000064642,0.00001866969,0.0003714873,0.00009729408,0.0001905163,0.0005132772,0.00009839968,0.00001407004],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005422721,"about_ca_system_score_gemma":0.006607014,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.06945659,"about_ca_topic_score_gemma":0.6832219,"domain_scores_codex":[0.998178,0.0001753362,0.0002768023,0.0007364198,0.0004030891,0.0002303424],"domain_scores_gemma":[0.9963354,0.001970055,0.00008078423,0.001220873,0.0001742747,0.0002186251],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00008963676,0.00005582127,0.1091578,0.00009579112,0.00002548996,0.00006449135,0.00001915247,0.00003755119,0.002600518,0.000317162,0.8841216,0.003414963],"study_design_scores_gemma":[0.002568923,0.00001505126,0.4455865,0.00004094175,0.00006773534,0.0001616947,0.0001651379,0.07966857,0.00006850252,0.0009225513,0.4705257,0.0002087516],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6085061,0.003552056,0.005324378,0.03616107,0.002039051,0.003107922,0.3400232,0.000189711,0.001096475],"genre_scores_gemma":[0.6801311,0.0006561057,0.003867983,0.007118463,0.000249378,0.0001327615,0.3055641,0.00002709654,0.002253033],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6137653,"threshold_uncertainty_score":0.9990246,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1385434489539665,"score_gpt":0.3767589396202392,"score_spread":0.2382154906662727,"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."}}