{"id":"W3164016997","doi":"10.3389/frma.2021.683212","title":"Profiling COVID-19 Genetic Research: A Data-Driven Study Utilizing Intelligent Bibliometrics","year":2021,"lang":"en","type":"article","venue":"Frontiers in Research Metrics and Analytics","topic":"SARS-CoV-2 and COVID-19 Research","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Australian Research Council; University of Technology Sydney; University of Sydney","keywords":"Pandemic; Bibliometrics; Public health; Biology; Human genetics; Disease; Political science; Evolutionary biology; Genetics; Coronavirus disease 2019 (COVID-19); Gene; Infectious disease (medical specialty); Medicine; Library science; Computer science","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":["metaresearch","bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.006574967,0.00058127,0.001234273,0.07675084,0.001223171,0.005421014,0.000755904,0.0009604151,0.001778424],"category_scores_gemma":[0.04253144,0.0001859057,0.001233815,0.1059401,0.0006724239,0.002785131,0.001945606,0.0005243514,0.0007266416],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002261625,"about_ca_system_score_gemma":0.003012187,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006975855,"about_ca_topic_score_gemma":0.008125984,"domain_scores_codex":[0.9906242,0.002621387,0.00178699,0.001152216,0.003362613,0.0004525522],"domain_scores_gemma":[0.9505044,0.03204855,0.008032211,0.002168135,0.005898399,0.00134831],"domain_codex":null,"domain_gemma":"evaluation","domain_candidate":"evaluation","domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000277774,0.0003619355,0.7720256,0.00360212,0.001369596,0.00106827,0.002368318,0.005189376,0.002517254,0.01087921,0.01885712,0.1814834],"study_design_scores_gemma":[0.0001004472,0.0002869475,0.8418031,0.0008933023,0.0009777409,0.001353847,0.007339795,0.0490375,0.002704533,0.01585684,0.07948142,0.0001646703],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8763115,0.01186556,0.01236575,0.004454306,0.000245333,0.000518954,0.07769462,0.0008406971,0.01570323],"genre_scores_gemma":[0.926768,0.004249552,0.01762983,0.0002323539,0.0003230272,0.0004216061,0.0489093,0.00008275082,0.001383549],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.993425,"threshold_uncertainty_score":0.03477222,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.609732203422397,"score_gpt":0.55750781381103,"score_spread":0.05222438961136699,"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."}}