{"id":"W3110302913","doi":"10.1016/j.socscimed.2020.113549","title":"Open government data, uncertainty and coronavirus: An infodemiological case study","year":2020,"lang":"en","type":"article","venue":"Social Science & Medicine","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":16,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo; Public Health Ontario; University of Toronto; McMaster University","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Transparency (behavior); Pandemic; Accountability; Government (linguistics); Open data; Open government; Public health; Population; Public relations; Value (mathematics); Business; Coronavirus disease 2019 (COVID-19); Political science; Actuarial science; Public economics; Medicine; Environmental health; Economics; Computer science; Law; Infectious disease (medical specialty)","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002481631,0.0001757635,0.0005388686,0.00002009043,0.0004541913,0.00005903327,0.001164556,0.00004702658,0.00029345],"category_scores_gemma":[0.001772324,0.0001209266,0.00001628912,0.0005781473,0.001811654,0.0005995817,0.001513324,0.0002348526,0.00001055275],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001685324,"about_ca_system_score_gemma":0.0003161757,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002022499,"about_ca_topic_score_gemma":0.000340209,"domain_scores_codex":[0.9970701,0.0001676805,0.0003566282,0.0009014446,0.00113299,0.0003712004],"domain_scores_gemma":[0.9982136,0.0001029194,0.0001313847,0.0005937591,0.0001003119,0.0008580097],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001090438,0.001090383,0.6656905,0.00006400984,0.00008700023,0.008982949,0.03988526,0.000004033291,0.00249398,0.0007987328,0.01483419,0.2649785],"study_design_scores_gemma":[0.01639661,0.01085505,0.6299717,0.0001186127,0.0006359277,0.001322942,0.1937766,0.00736723,0.00002032993,0.000318796,0.1382324,0.0009836497],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9866828,0.0001117681,0.00004673832,0.006752216,0.000117351,0.001147285,0.0002288871,0.00007966632,0.004833321],"genre_scores_gemma":[0.9914488,0.00002489551,0.00006592565,0.007764311,0.0005457491,0.00001836491,0.00009589789,0.000009152319,0.00002685917],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2639948,"threshold_uncertainty_score":0.6675116,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2051445018283466,"score_gpt":0.4515485926020226,"score_spread":0.2464040907736759,"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."}}