{"id":"W3123035233","doi":"10.3390/ijerph18030952","title":"A Bibliometric Network Analysis of Coronavirus during the First Eight Months of COVID-19 in 2020","year":2021,"lang":"en","type":"article","venue":"International Journal of Environmental Research and Public Health","topic":"COVID-19 Clinical Research Studies","field":"Medicine","cited_by":94,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University; Lakehead University","funders":"","keywords":"Thematic analysis; Coronavirus disease 2019 (COVID-19); Thematic map; Coronavirus; Pandemic; Bibliometrics; Futures contract; Web of science; Data science; Medicine; Computer science; Geography; Qualitative research; Sociology; Disease; Social science; Business; Data mining; Pathology; Cartography; Meta-analysis","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.004285352,0.000430282,0.0007725106,0.07266093,0.001224493,0.003581184,0.0005006405,0.0006815055,0.002962448],"category_scores_gemma":[0.02399349,0.0001892944,0.001267681,0.1127885,0.0005701089,0.002744645,0.0016118,0.0004281082,0.0004633437],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002989297,"about_ca_system_score_gemma":0.003243083,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01205544,"about_ca_topic_score_gemma":0.01486524,"domain_scores_codex":[0.9953623,0.00105029,0.000935914,0.000730457,0.001577787,0.0003431634],"domain_scores_gemma":[0.9755201,0.01548698,0.004262363,0.0005284788,0.003643165,0.0005588282],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004827059,0.0001109167,0.6946102,0.01551335,0.001981412,0.001310345,0.009336546,0.005077269,0.003082481,0.008459456,0.02460303,0.2354323],"study_design_scores_gemma":[0.00002774446,0.0001618421,0.8800094,0.00268867,0.001685402,0.001417903,0.01063029,0.009415432,0.001446942,0.005035121,0.0873727,0.0001087202],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8549024,0.03172618,0.004358675,0.003462188,0.0002841536,0.0004506847,0.07873023,0.0003336491,0.02575183],"genre_scores_gemma":[0.9408588,0.01632562,0.006054941,0.0001547988,0.0002353458,0.0005580041,0.03329823,0.00006687573,0.002447437],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9273391,"threshold_uncertainty_score":0.02397054,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1303412672450861,"score_gpt":0.4927674024976287,"score_spread":0.3624261352525425,"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."}}