{"id":"W2034270607","doi":"10.1371/journal.pone.0109209","title":"Improving Google Flu Trends Estimates for the United States through Transformation","year":2014,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"China Scholarship Council; Alberta Innovates; Alberta Innovates - Technology Futures","keywords":"Demography; Seasonal influenza; Influenza season; Medicine; Disease control; Statistics; Incidence (geometry); Geography; Coronavirus disease 2019 (COVID-19); Mathematics; Environmental health; Virology; Vaccination; Disease; Internal medicine; Influenza vaccine","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003223953,0.0007901565,0.0006848788,0.005088047,0.0003249581,0.002076988,0.0006305841,0.0004388022,0.00306222],"category_scores_gemma":[0.02414756,0.0004024829,0.001544035,0.006611953,0.0002646566,0.002162805,0.001396551,0.0009683205,0.002618182],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001333088,"about_ca_system_score_gemma":0.001818821,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.09891885,"about_ca_topic_score_gemma":0.05367881,"domain_scores_codex":[0.9977764,0.0005987968,0.000315229,0.0004272914,0.0007408889,0.0001413144],"domain_scores_gemma":[0.9958573,0.000922832,0.000586903,0.0005753537,0.00198722,0.00007044626],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000218957,0.0001416445,0.3334877,0.0008331981,0.000817902,0.0003346473,0.0009384978,0.08513476,0.001018193,0.02005452,0.194098,0.362922],"study_design_scores_gemma":[0.0001224713,0.0001899739,0.2959401,0.0005809106,0.0003688083,0.0005200569,0.00143706,0.3837146,0.002549954,0.01853074,0.2958004,0.0002449589],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.3707896,0.007339487,0.2490319,0.007848653,0.001938848,0.001011235,0.2697489,0.0258155,0.06647579],"genre_scores_gemma":[0.7275282,0.002424246,0.09710959,0.0006908482,0.0004005885,0.0007060882,0.1644574,0.001706252,0.004976795],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09891885,"threshold_uncertainty_score":0.1966861,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04426050112740804,"score_gpt":0.2750779780420251,"score_spread":0.2308174769146171,"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."}}