{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01327861,0.0002855598,0.00119444,0.000544091,0.0002874742,0.0002279395,0.0004702675,0.0001126125,0.00003664071],"category_scores_gemma":[0.002766221,0.0001754464,0.0001915955,0.0007068151,0.0002545305,0.001531361,0.0001056369,0.001096492,0.0000161921],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000476215,"about_ca_system_score_gemma":0.006282401,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001218746,"about_ca_topic_score_gemma":0.0000208239,"domain_scores_codex":[0.9924878,0.0003716887,0.00488033,0.00007555859,0.001196424,0.0009882217],"domain_scores_gemma":[0.9921895,0.0002783868,0.004174992,0.0005858543,0.0008946555,0.001876641],"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":[0.0001532453,0.0005763122,0.01121226,0.001231085,0.0001941514,0.000007091403,0.01295282,0.00002777774,4.861955e-7,0.002589009,0.0471874,0.9238684],"study_design_scores_gemma":[0.01218438,0.00198239,0.05733227,0.0004963439,0.00004021102,0.002731658,0.008337443,0.01938713,2.975123e-7,0.0004238118,0.8968037,0.000280311],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.3266954,0.008667042,0.05383706,0.6025584,0.001002886,0.002359828,0.0006627444,0.0003042785,0.003912312],"genre_scores_gemma":[0.5542859,0.03949675,0.1288322,0.2747295,0.001458792,0.000008301921,0.0008437017,0.0000660601,0.0002788048],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.923588,"threshold_uncertainty_score":0.9993511,"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."}}