{"id":"W2883375453","doi":"10.14745/ccdr.v41i09a02","title":"Big Data and the Global Public Health Intelligence Network (GPHIN)","year":2015,"lang":"en","type":"article","venue":"Canada Communicable Disease Report","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":110,"is_retracted":false,"has_abstract":true,"ca_institutions":"Response Biomedical (Canada); Public Health Agency of Canada; Western University","funders":"Public Health Agency; Public Health Agency of Canada","keywords":"Public health; Outbreak; Big data; Emerging infectious disease; Global health; Infectious disease (medical specialty); International Health Regulations; Situation awareness; Environmental health; Globalization; Capacity building; Public health surveillance; Social media; Business; Disease; Medicine; Political science; Computer science; Coronavirus disease 2019 (COVID-19); Economic growth; Engineering; Virology; Data mining; Pathology; Economics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"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":[0.002918253,0.0002208221,0.0005683608,0.00001919054,0.0003009668,0.00009483464,0.001256069,0.00003254018,0.0000204836],"category_scores_gemma":[0.00384584,0.0001630624,0.00004727611,0.0005867694,0.0005036822,0.0001601221,0.00196263,0.000273769,0.000004617476],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000759592,"about_ca_system_score_gemma":0.02388363,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.3516053,"about_ca_topic_score_gemma":0.5770854,"domain_scores_codex":[0.9969646,0.0004913146,0.0006537961,0.0004860398,0.0008042019,0.0006000113],"domain_scores_gemma":[0.9899449,0.0002000132,0.0003284502,0.006709249,0.000361548,0.002455808],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000603426,0.0001756507,0.4320872,0.0001509329,0.0002966021,0.003274522,0.00004894609,0.00009849847,3.763147e-8,0.002363994,0.5222418,0.03865841],"study_design_scores_gemma":[0.001015472,0.00002313217,0.0644082,0.0001106139,0.00009276873,0.0006071238,0.0003365535,0.0051939,5.668457e-8,0.0009611021,0.9270468,0.0002043077],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.08655687,0.3883971,0.008355195,0.4198952,0.005971549,0.007161979,0.01082869,0.001138417,0.07169507],"genre_scores_gemma":[0.985512,0.001080194,0.0003870856,0.008510346,0.0002789572,0.00003321616,0.003859614,0.00002620315,0.0003124188],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8989551,"threshold_uncertainty_score":0.9816501,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1688642691876727,"score_gpt":0.3365549164026105,"score_spread":0.1676906472149378,"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."}}