{"id":"W4287219529","doi":"10.5121/csit.2022.121219","title":"A Summary of Covid-19 Datasets","year":2022,"lang":"en","type":"article","venue":"","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute of Aging; University of Victoria; University of Toronto; Toronto Metropolitan University","funders":"Canadian Institutes of Health Research","keywords":"Coronavirus disease 2019 (COVID-19); Variety (cybernetics); Data science; Table (database); Computer science; Open research; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); 2019-20 coronavirus outbreak; Knowledge management; World Wide Web; Data mining; Artificial intelligence; 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":["open_science"],"consensus_categories":[],"category_scores_codex":[0.006515828,0.001861195,0.001788188,0.01138266,0.002843427,0.004200648,0.004466881,0.002857371,0.0225278],"category_scores_gemma":[0.03189922,0.0008350318,0.002058373,0.01703288,0.001003864,0.00375903,0.003889421,0.003207637,0.02455459],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003027128,"about_ca_system_score_gemma":0.005935932,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03013823,"about_ca_topic_score_gemma":0.03920016,"domain_scores_codex":[0.9903029,0.002145571,0.00244546,0.001881904,0.002464138,0.000760088],"domain_scores_gemma":[0.9880726,0.003987538,0.00105045,0.002654392,0.003387658,0.0008473702],"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":[0.0002371529,0.0001106402,0.006047828,0.001601993,0.0001156869,0.0001475348,0.0001122848,0.001733879,0.0004910736,0.002686416,0.9696707,0.01704482],"study_design_scores_gemma":[0.0001875227,0.00007557534,0.01046629,0.0009405648,0.00007987755,0.0003564727,0.0004247734,0.002398239,0.0009367692,0.004524549,0.9794946,0.0001146791],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.001329408,0.001059409,0.001292043,0.0007429078,0.0002152907,0.0002944368,0.9910778,0.001088072,0.002900692],"genre_scores_gemma":[0.001541546,0.0003207658,0.002878984,0.0002839094,0.00003444945,0.0004127977,0.9939366,0.0001068734,0.0004841769],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.9955331,"threshold_uncertainty_score":0.07536292,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03755001519600972,"score_gpt":0.3441188962438844,"score_spread":0.3065688810478747,"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."}}