{"id":"W4206039573","doi":"10.23889/ijpds.v5i4.1682","title":"Pivoting data and analytic capacity to support Ontario’s COVID-19 response","year":2022,"lang":"en","type":"article","venue":"International Journal for Population Data Science","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Health Sciences Centre; Vector Institute; Trillium Health Centre; University of Toronto; University Health Network; Public Health Ontario; Hospital for Sick Children; Sunnybrook Health Science Centre; McMaster University; Women's College Hospital","funders":"Agency for Healthcare Research and Quality","keywords":"Public health; Pandemic; Population; Health care; Analytics; Population health; Public relations; Business; Political science; Environmental health; Coronavirus disease 2019 (COVID-19); Medicine; Nursing; Data science; Computer science; Disease; 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","sts"],"consensus_categories":[],"category_scores_codex":[0.01578888,0.0001222852,0.0002110722,0.0003373569,0.001485132,0.0002555309,0.003764311,0.00002030561,0.0003350099],"category_scores_gemma":[0.06925625,0.0001078978,0.00003411615,0.0003481382,0.000146602,0.00124975,0.0054867,0.0002755714,0.000003361032],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001613562,"about_ca_system_score_gemma":0.0006223646,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005917625,"about_ca_topic_score_gemma":0.01071506,"domain_scores_codex":[0.9970167,0.0001997204,0.0006181971,0.0006880952,0.001188069,0.000289223],"domain_scores_gemma":[0.9954477,0.002732769,0.0004176866,0.0008300866,0.0002375214,0.0003342176],"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":[0.002240949,0.0003363244,0.7928723,0.00004580905,0.0002271803,0.0001083177,0.004676656,0.007501139,0.00101947,0.04432713,0.1388694,0.007775351],"study_design_scores_gemma":[0.001281634,0.0004720372,0.3057705,0.00002961038,0.00008611905,0.0007402432,0.0007641385,0.07551602,0.00000839431,0.1462155,0.468598,0.0005177455],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8987742,0.00001196225,0.07503817,0.02166373,0.001450231,0.0004106813,0.002554889,0.00004747061,0.00004865149],"genre_scores_gemma":[0.9624501,0.000003283238,0.0329342,0.003911045,0.0001493993,0.00001444357,0.0002490505,0.000008358749,0.0002801067],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4871017,"threshold_uncertainty_score":0.9998148,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6458393543368621,"score_gpt":0.5551504100211131,"score_spread":0.09068894431574903,"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."}}