{"id":"W4302275377","doi":"10.1093/database/baac084","title":"Overview of the COVID-19 text mining tool interactive demonstration track in BioCreative VII","year":2022,"lang":"en","type":"article","venue":"Database","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Institute for Research in Immunology and Cancer","funders":"U.S. National Library of Medicine; National Institute of General Medical Sciences; National Human Genome Research Institute; Canadian Institutes of Health Research; National Institutes of Health","keywords":"Coronavirus disease 2019 (COVID-19); Computer science; Track (disk drive); Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); 2019-20 coronavirus outbreak; Information retrieval; Natural language processing; Artificial intelligence; World Wide Web; Virology; Medicine","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.004865448,0.001856215,0.001091033,0.004230924,0.001078277,0.0041075,0.004466458,0.002000463,0.08958161],"category_scores_gemma":[0.01010963,0.001426042,0.001485525,0.002278947,0.0005038831,0.004605745,0.002799347,0.001913893,0.04419575],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008259601,"about_ca_system_score_gemma":0.001703863,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005144983,"about_ca_topic_score_gemma":0.006382781,"domain_scores_codex":[0.9974135,0.0004676345,0.0003375694,0.0005885364,0.001009134,0.0001836989],"domain_scores_gemma":[0.9917549,0.005209488,0.0002800003,0.0007937794,0.001277706,0.0006842291],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.002078688,0.001540407,0.006586143,0.003352842,0.0002397862,0.002019366,0.002726957,0.00338381,0.03206155,0.004220346,0.5974427,0.3443474],"study_design_scores_gemma":[0.0008245967,0.001077142,0.009266864,0.0007348295,0.0001096897,0.001607309,0.0005503328,0.04023557,0.02490327,0.003357516,0.9170151,0.0003177933],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"software","genre_gemma":"other","genre_scores_codex":[0.02297851,0.001436082,0.3588969,0.001496648,0.0007053954,0.005642165,0.09800353,0.4615268,0.04931394],"genre_scores_gemma":[0.05322511,0.00105182,0.6056722,0.001534433,0.0002816302,0.008377775,0.211608,0.04598839,0.07226072],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.08958161,"threshold_uncertainty_score":0.2996804,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05752538837657176,"score_gpt":0.345342854236081,"score_spread":0.2878174658595092,"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."}}