{"id":"W3015874364","doi":"10.48550/arxiv.2004.04596","title":"Global Public Health Surveillance using Media Reports: Redesigning GPHIN","year":2020,"lang":"en","type":"preprint","venue":"PubMed","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Underpinning; Mainstream; Trustworthiness; Public health; Politics; Public relations; Political science; Public health surveillance; Computer science; Internet privacy; Engineering; Medicine","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":[0.003238368,0.0006836031,0.001842506,0.0001983797,0.0001426144,0.000242542,0.0005146347,0.0003865492,0.00005825336],"category_scores_gemma":[0.006589104,0.0007144675,0.0004166871,0.0008589983,0.0001904655,0.0001629901,0.00137132,0.0009297212,0.00001810501],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001954784,"about_ca_system_score_gemma":0.002778789,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006300525,"about_ca_topic_score_gemma":0.0002156234,"domain_scores_codex":[0.9931003,0.0005829926,0.001574939,0.001890861,0.001323291,0.001527587],"domain_scores_gemma":[0.9937133,0.0001564386,0.00150526,0.002067276,0.0003742059,0.00218353],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003071748,0.000369065,0.7799167,0.00257262,0.0009778963,0.003824014,0.0003543961,0.0002217174,0.00001284337,0.0001278498,0.06070609,0.1506097],"study_design_scores_gemma":[0.0007858239,0.00002170127,0.9713323,0.0002167492,0.00007048945,0.000400756,0.00005620262,0.0006339028,0.000005288129,0.0008462339,0.02498654,0.0006439757],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6182096,0.0538331,0.0388542,0.1572685,0.03152597,0.03665027,0.01858261,0.01204967,0.03302606],"genre_scores_gemma":[0.9851424,0.0002984939,0.004899843,0.003232028,0.001584001,0.0009777447,0.003681784,0.0001257837,0.000057931],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3669328,"threshold_uncertainty_score":0.9995306,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1376487322422343,"score_gpt":0.3181640899197627,"score_spread":0.1805153576775285,"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."}}