{"id":"W3199323206","doi":"10.14745/ccdr.v47i09a01","title":"Crowdsourced disease surveillance success story: The FluWatchers program","year":2021,"lang":"en","type":"article","venue":"Canada Communicable Disease Report","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Public Health Agency of Canada","funders":"","keywords":"Preparedness; Disease surveillance; Pandemic; Coronavirus disease 2019 (COVID-19); Disease; Medicine; Computer science; Data science; Political science; Infectious disease (medical specialty); Pathology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007857526,0.0004230406,0.0006981062,0.00004430309,0.0006547961,0.0001395688,0.0008581953,0.00006176926,0.000417279],"category_scores_gemma":[0.003432381,0.0003506118,0.0002948688,0.0008471636,0.0003996254,0.0001457159,0.00060118,0.000655231,0.00001446918],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006682827,"about_ca_system_score_gemma":0.01686187,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.05772703,"about_ca_topic_score_gemma":0.2676257,"domain_scores_codex":[0.9958138,0.0007241578,0.0006467002,0.0007537421,0.001276025,0.0007855068],"domain_scores_gemma":[0.9900463,0.0003693848,0.0003248858,0.006111205,0.0009806136,0.002167614],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006469063,0.0007367577,0.7704372,0.0005050785,0.0005010733,0.05152488,0.00003892129,0.000266753,0.00006684434,0.00008514313,0.1703384,0.004852023],"study_design_scores_gemma":[0.0006265719,0.00001181469,0.5026985,0.0001200991,0.0001735896,0.0001638029,0.0001534377,0.0004633337,0.00002230448,0.00001796919,0.4952484,0.0003002135],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9279679,0.02373296,0.00002760925,0.03400793,0.0008082573,0.002116729,0.001421471,0.0007732068,0.009143909],"genre_scores_gemma":[0.9854534,0.0003056618,0.0001686904,0.002907421,0.0001528035,0.0003580881,0.004176812,0.00009707544,0.00638011],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.32491,"threshold_uncertainty_score":0.9998946,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01487213737107142,"score_gpt":0.2773931183646199,"score_spread":0.2625209809935485,"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."}}