{"id":"W4312242765","doi":"10.2196/42292","title":"Big Data and Infectious Disease Epidemiology: Bibliometric Analysis and Research Agenda","year":2022,"lang":"en","type":"article","venue":"Interactive Journal of Medical Research","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Big data; Disease surveillance; Data science; Infectious disease (medical specialty); Informatics; Epidemiology; Disease; Pandemic; Health informatics; The Internet; Public health; Medicine; Computer science; Political science; Coronavirus disease 2019 (COVID-19); World Wide Web; Data mining; Pathology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","bibliometrics","research_integrity","insufficient_payload"],"consensus_categories":["metaresearch","bibliometrics"],"category_scores_codex":[0.05942258,0.0001469625,0.0008420181,0.07380892,0.000444821,0.00007839372,0.001136729,0.00008934459,0.00186668],"category_scores_gemma":[0.1768485,0.0001140394,0.0001542498,0.0735261,0.001129818,0.0003111811,0.004547233,0.004883637,0.00001335007],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003822568,"about_ca_system_score_gemma":0.001788486,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004755083,"about_ca_topic_score_gemma":0.00006779132,"domain_scores_codex":[0.9841964,0.007393199,0.000885161,0.000624228,0.006171896,0.0007290845],"domain_scores_gemma":[0.9604492,0.03162788,0.0004469963,0.001431135,0.002670433,0.003374365],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002620733,0.0007221192,0.5942931,0.0001164587,0.001759359,0.004276163,0.0001811705,0.000003837166,0.00008474886,0.00004627209,0.05731683,0.3385792],"study_design_scores_gemma":[0.001842195,0.001208318,0.8478453,0.000152285,0.0003070702,0.0007168249,0.0008455442,0.004111913,0.000009173326,0.0008848808,0.1419676,0.0001088958],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9621354,0.01384842,0.0008224233,0.02138047,0.0003493751,0.0003443218,0.0004834325,0.00001506867,0.0006210658],"genre_scores_gemma":[0.9904843,0.007948252,0.00009863577,0.0005692371,0.0005700036,0.00002109921,0.0001099413,0.00002140099,0.0001771575],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3384703,"threshold_uncertainty_score":0.9990457,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3080709112029464,"score_gpt":0.5562182349421962,"score_spread":0.2481473237392499,"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."}}