{"id":"W2147627663","doi":"10.3201/eid1505.081114","title":"Use of Unstructured Event-Based Reports for Global Infectious Disease Surveillance","year":2009,"lang":"en","type":"review","venue":"Emerging infectious diseases","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":203,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Public Health Agency of Canada; University Health Network","funders":"U.S. National Library of Medicine","keywords":"Public health; Public health surveillance; Disease surveillance; Infectious disease (medical specialty); Context (archaeology); Outbreak; The Internet; Event monitoring; Computer science; Information Dissemination; International Health Regulations; Event (particle physics); Psychological intervention; Unstructured data; Data science; Medicine; Process (computing); Disease; Data mining; World Wide Web; Big data; Geography; Pathology","routes":{"ca_aff":true,"ca_fund":false,"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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002863313,0.001251469,0.00394382,0.0004849305,0.0002310538,0.0001147369,0.0002584198,0.0003477691,0.0001139887],"category_scores_gemma":[0.003928171,0.001149714,0.00290099,0.001324402,0.000258933,0.0002463016,0.0001124842,0.0003384097,0.00001347207],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007706915,"about_ca_system_score_gemma":0.002929436,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008945701,"about_ca_topic_score_gemma":0.00007723389,"domain_scores_codex":[0.9942719,0.0004311849,0.00205046,0.00152003,0.0008424221,0.0008840498],"domain_scores_gemma":[0.9933749,0.0005632142,0.002041534,0.002179753,0.0006851093,0.001155457],"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":[0.0004268879,0.001677847,0.1721892,0.05487236,0.001579772,0.0009850392,0.000005282983,0.001300025,5.927314e-7,0.0001556615,0.01283381,0.7539735],"study_design_scores_gemma":[0.001560576,0.0003856749,0.0342127,0.0113004,0.005678212,0.0002564747,0.000001333497,0.0002041778,6.205855e-7,0.0004202827,0.9447522,0.001227305],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.000819481,0.9806846,0.001435358,0.0000420335,0.001576263,0.004597593,0.009498576,0.001205223,0.0001408187],"genre_scores_gemma":[0.03705126,0.9461076,0.0002087819,0.0003898226,0.0008781781,0.0008666125,0.01404841,0.000288297,0.0001609804],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9319184,"threshold_uncertainty_score":0.9990953,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02745826120509893,"score_gpt":0.3412254162712001,"score_spread":0.3137671550661011,"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."}}