{"id":"W3208351790","doi":"10.18280/ijdne.160507","title":"Large-Scale Bibliometric Analysis of Coronavirus","year":2021,"lang":"en","type":"article","venue":"International Journal of Design & Nature and Ecodynamics","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Qassim University","keywords":"Pandemic; Coronavirus disease 2019 (COVID-19); Coronavirus; Middle East respiratory syndrome; Scopus; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); China; 2019-20 coronavirus outbreak; Citation; Bibliometrics; Geography; Middle East respiratory syndrome coronavirus; Citation impact; Virology; Medicine; Demography; MEDLINE; Disease; Library science; Biology; Infectious disease (medical specialty); Pathology; Outbreak; Sociology; Archaeology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.007459948,0.0008281561,0.00195405,0.1680169,0.001393147,0.004366862,0.0007957584,0.0006971565,0.00387063],"category_scores_gemma":[0.05615807,0.0002654246,0.001948913,0.1997821,0.0007994617,0.002812372,0.002253432,0.0003891738,0.0006469776],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002224832,"about_ca_system_score_gemma":0.003798912,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006192177,"about_ca_topic_score_gemma":0.006775875,"domain_scores_codex":[0.9848903,0.003574173,0.003745397,0.001755967,0.005480526,0.0005536925],"domain_scores_gemma":[0.9067115,0.06902746,0.01198581,0.002388249,0.008776346,0.001110677],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005397995,0.0002560287,0.7004592,0.0358008,0.007588004,0.001604402,0.003524159,0.005414649,0.003220893,0.006353173,0.01860254,0.2166363],"study_design_scores_gemma":[0.00009699231,0.0002553975,0.9204574,0.004009346,0.004815493,0.001724583,0.005079824,0.01293923,0.002041184,0.007545612,0.04087578,0.0001592585],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8008777,0.07464357,0.004821435,0.001491024,0.0002098752,0.0005622373,0.1000194,0.0004960343,0.01687871],"genre_scores_gemma":[0.9449353,0.01517086,0.004244793,0.00008103458,0.0002218465,0.0004555286,0.0339668,0.00005274188,0.0008712512],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8319831,"threshold_uncertainty_score":0.03945249,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1086013958028587,"score_gpt":0.4102598940189482,"score_spread":0.3016584982160895,"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."}}