{"id":"W2143390259","doi":"10.5210/ojphi.v1i1.2778","title":"Public Health Informatics and the H1N1 Pandemic","year":2009,"lang":"en","type":"article","venue":"Online Journal of Public Health Informatics","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Public health; Pandemic; Medicine; Preparedness; Health informatics; Population; Influenza A virus subtype H5N1; Public health informatics; Public relations; Medical emergency; Health policy; Environmental health; International health; Disease; Political science; Infectious disease (medical specialty); Coronavirus disease 2019 (COVID-19); Nursing; Virology; Pathology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007250909,0.0002522476,0.0002877981,0.001927209,0.001749231,0.008396184,0.0005963857,0.003944663,0.008334257],"category_scores_gemma":[0.02677511,0.0003006869,0.0003260187,0.002035761,0.004123526,0.007426844,0.003025866,0.006260679,0.001118557],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003458286,"about_ca_system_score_gemma":0.005511662,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004090154,"about_ca_topic_score_gemma":0.005305112,"domain_scores_codex":[0.9951768,0.003274166,0.0002236544,0.0002528532,0.0008311995,0.0002413778],"domain_scores_gemma":[0.9684198,0.02445919,0.001386793,0.001113737,0.00214005,0.002480451],"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":[0.00003033102,0.00005912395,0.003622087,0.0002888205,0.00001513457,0.0001813171,0.001579354,0.0002071255,0.00006879028,0.1282814,0.7175852,0.1480813],"study_design_scores_gemma":[0.00001995303,0.00002716188,0.003808486,0.001559484,0.00001616163,0.0003608429,0.002231739,0.0004629158,0.0001266466,0.06499059,0.9263785,0.00001750608],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.001418071,0.02374678,0.0009321927,0.9331308,0.006159899,0.00001150256,0.00009192439,0.0000789182,0.03442995],"genre_scores_gemma":[0.2269198,0.168648,0.005758029,0.4861332,0.07475732,0.0001709073,0.0005735944,0.0001602203,0.03687891],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008396184,"threshold_uncertainty_score":0.03834689,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.117245230303292,"score_gpt":0.3706207218342925,"score_spread":0.2533754915310006,"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."}}