{"id":"W2893214481","doi":"10.3390/su10103414","title":"Global Research on Syndromic Surveillance from 1993 to 2017: Bibliometric Analysis and Visualization","year":2018,"lang":"en","type":"article","venue":"Sustainability","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Institute for Information and Communications Technology Promotion; Ministry of Science and ICT, South Korea; Iran Telecommunication Research Center; National Research Foundation of Korea; Ministry of Science, ICT and Future Planning; National Research Foundation","keywords":"Scopus; Bibliometrics; Data science; Web of science; Flourishing; Citation; Citation analysis; Publishing; Visualization; Field (mathematics); Analytics; Computer science; Library science; Data mining; MEDLINE; Political science; Psychology","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":["bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.003343007,0.0006220749,0.000659448,0.09615699,0.0007364436,0.004752896,0.0004620489,0.0005442999,0.002972132],"category_scores_gemma":[0.01665819,0.0001534026,0.0009412285,0.1304347,0.0004877211,0.002999775,0.001702327,0.0004407043,0.0008576082],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002237828,"about_ca_system_score_gemma":0.003014072,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0127911,"about_ca_topic_score_gemma":0.01447913,"domain_scores_codex":[0.996784,0.0005738075,0.0006426949,0.000301387,0.001438422,0.0002597684],"domain_scores_gemma":[0.9845282,0.006160382,0.004169271,0.000802593,0.003836849,0.0005025906],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002169764,0.0001315208,0.4709499,0.005737116,0.0007060357,0.0008718935,0.009198506,0.006710074,0.003514583,0.02822878,0.05527909,0.4184555],"study_design_scores_gemma":[0.00002260591,0.0001246712,0.773035,0.001734646,0.0003901989,0.001043197,0.01223705,0.0118922,0.00370111,0.01117631,0.184515,0.0001279393],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7118213,0.02608936,0.00962258,0.007942927,0.000504045,0.0003469879,0.1678506,0.00221869,0.07360347],"genre_scores_gemma":[0.9003468,0.01373061,0.01375803,0.0001596989,0.0004069867,0.0003116498,0.06670848,0.0001617716,0.004416089],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.903843,"threshold_uncertainty_score":0.0254333,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04081826464100322,"score_gpt":0.4362929666343069,"score_spread":0.3954747019933037,"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."}}