{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01705762,0.001058063,0.0005226436,0.001440977,0.004758943,0.004184805,0.002221185,0.002861484,0.006770793],"category_scores_gemma":[0.03657351,0.0003595563,0.0008202756,0.002168982,0.002485116,0.004751349,0.006593922,0.005126087,0.003751757],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005181506,"about_ca_system_score_gemma":0.01475247,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.256364,"about_ca_topic_score_gemma":0.3672889,"domain_scores_codex":[0.9929355,0.002778228,0.0001589726,0.0009030189,0.002482166,0.0007421834],"domain_scores_gemma":[0.9595125,0.01697847,0.0008259626,0.004694169,0.01047147,0.007517406],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.0002113175,0.0001102359,0.005286839,0.0001549034,0.00005916017,0.00008885119,0.000956893,0.0008508015,0.0003241792,0.004880745,0.9317141,0.05536192],"study_design_scores_gemma":[0.0002913301,0.0002657805,0.01599371,0.0004709156,0.00007214372,0.0001265705,0.005361473,0.007081355,0.001535585,0.01381584,0.9548345,0.0001507246],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.04615924,0.009802588,0.01925856,0.6674405,0.01645261,0.001165144,0.1049656,0.009549925,0.1252058],"genre_scores_gemma":[0.5376569,0.009746302,0.05146826,0.1202059,0.00649379,0.002477176,0.1763319,0.003108247,0.09251153],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.743636,"threshold_uncertainty_score":0.5097436,"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."}}