{"id":"W2167286483","doi":"10.5210/ojphi.v5i1.4470","title":"Influenza Forecasting with Google Flu Trends","year":2013,"lang":"en","type":"article","venue":"Online Journal of Public Health Informatics","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Division of Civil, Mechanical and Manufacturing Innovation; Natural Sciences and Engineering Research Council of Canada; U.S. Department of Homeland Security; National Science Foundation","keywords":"Computer science; Warning system; Selection (genetic algorithm); Seasonal influenza; Model selection; Coronavirus disease 2019 (COVID-19); Data mining; Data science; Medicine; Machine learning; Infectious disease (medical specialty); Telecommunications; Disease","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00165893,0.0001963264,0.0006496625,0.0005665737,0.0000982001,0.0001051672,0.0002439872,0.00006828587,0.0003670658],"category_scores_gemma":[0.0008396464,0.0001319857,0.000110666,0.0006812338,0.00007509923,0.001485266,0.00006180448,0.0005414675,0.00004569662],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002613355,"about_ca_system_score_gemma":0.001969049,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002903451,"about_ca_topic_score_gemma":0.00002011924,"domain_scores_codex":[0.996392,0.00007639285,0.002097453,0.00006710141,0.0007672653,0.0005998443],"domain_scores_gemma":[0.9952472,0.00009437961,0.001961386,0.0003531328,0.001131585,0.001212284],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000064395,0.0004954753,0.05013608,0.0008180349,0.0001933678,0.00002428915,0.00187232,0.0001101363,0.000003641102,0.00007276545,0.04931967,0.8968898],"study_design_scores_gemma":[0.005166811,0.002594801,0.1339931,0.0007679707,0.00004759548,0.0021182,0.003006193,0.02444237,0.000003668193,0.00003246464,0.8275543,0.0002725578],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9607034,0.0007857584,0.007804788,0.02379421,0.0003283218,0.0004955799,0.0002487153,0.0001174408,0.00572181],"genre_scores_gemma":[0.5241198,0.0006353557,0.421065,0.05032112,0.001827691,0.00002084856,0.0007271806,0.0001341167,0.001148874],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8966173,"threshold_uncertainty_score":0.538222,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1193108027593693,"score_gpt":0.3546440358282462,"score_spread":0.2353332330688769,"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."}}