{"id":"W3189604282","doi":"10.17269/s41997-021-00560-1","title":"Detection of COVID-19 case clusters in Québec, May–October 2020","year":2021,"lang":"en","type":"article","venue":"Canadian Journal of Public Health","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Université de Montréal; Université Laval; Institut National de Santé Publique du Québec","funders":"Ministère de la Santé; Ministère de la Santé et des Services sociaux","keywords":"Scan statistic; Public health; Coronavirus disease 2019 (COVID-19); Cluster (spacecraft); Statistic; Public health interventions; Compromise; Poisson distribution; Software deployment; Public health surveillance; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Geography; 2019-20 coronavirus outbreak; Computer science; Environmental health; Political science; Medicine; Statistics; Nursing; Virology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001703762,0.0001008769,0.0004316764,0.0004858715,0.00007708553,0.00002582282,0.0001000871,0.00006450088,0.0006335085],"category_scores_gemma":[0.004724095,0.0001017703,0.0001033664,0.0006808614,0.00009015979,0.000164224,0.00001252406,0.0003444035,0.00000739338],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002662176,"about_ca_system_score_gemma":0.07435793,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.3601626,"about_ca_topic_score_gemma":0.9680542,"domain_scores_codex":[0.9980323,0.0004152486,0.0007075352,0.0001574789,0.0002243963,0.0004630713],"domain_scores_gemma":[0.9941424,0.0001347199,0.0003632514,0.0002746388,0.0003684425,0.004716536],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00013628,0.0002408456,0.6208835,0.001591025,0.0002544565,0.1082109,0.005363791,0.0001441691,0.0002074305,0.0001397943,0.1140259,0.1488019],"study_design_scores_gemma":[0.003386861,0.0003778581,0.1100724,0.000187729,0.00002762874,0.03901079,0.004978916,0.0001810897,0.00003581458,0.00002373049,0.8415415,0.0001755836],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8509091,0.005712776,0.001159986,0.1407899,0.0005293859,0.0002235102,0.0002491527,0.00001028382,0.0004159027],"genre_scores_gemma":[0.9894094,0.0001350416,0.0002143274,0.009860009,0.0001507962,0.000001725494,0.00002721227,0.0000164848,0.0001850126],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7275156,"threshold_uncertainty_score":0.9308896,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05051174995579253,"score_gpt":0.329057318769777,"score_spread":0.2785455688139844,"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."}}