{"id":"W4398643520","doi":"10.7910/dvn/1pdmpq","title":"ARCH COVID-19","year":2022,"lang":"en","type":"dataset","venue":"Harvard Dataverse","topic":"Data Analysis with R","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Coronavirus disease 2019 (COVID-19); Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); 2019-20 coronavirus outbreak; Arch; Virology; Cartography; Geography; Medicine; Archaeology; Internal medicine; Outbreak; Infectious disease (medical specialty)","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":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.006503389,0.001725206,0.001598916,0.006303794,0.001212869,0.00381823,0.002959743,0.001352682,0.417331],"category_scores_gemma":[0.04142018,0.001662029,0.001672112,0.008288619,0.0005665603,0.002488066,0.003189507,0.003057908,0.2505828],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001644691,"about_ca_system_score_gemma":0.005995028,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01400271,"about_ca_topic_score_gemma":0.02812129,"domain_scores_codex":[0.9958265,0.001028633,0.0009030026,0.0008984495,0.0008621332,0.0004812736],"domain_scores_gemma":[0.9741357,0.01132749,0.002228815,0.006217231,0.004912752,0.00117808],"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":[0.0000591665,0.00001462093,0.0008539719,0.000281912,0.00002365632,0.00001298977,0.00003166538,0.0001119033,0.00004739016,0.0007623541,0.9954293,0.002371073],"study_design_scores_gemma":[0.0003219171,0.00003462651,0.004225167,0.0003711703,0.00005038056,0.00007624908,0.0001358241,0.000430345,0.0004478199,0.005216293,0.9886281,0.00006219679],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001301221,0.00001671611,0.0006041653,0.000106444,0.00004103902,0.00006695135,0.9951969,0.002097246,0.001740478],"genre_scores_gemma":[0.001565421,0.00006385896,0.004056427,0.0003256582,0.00006036297,0.001239604,0.9823065,0.00501916,0.005362973],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.582669,"threshold_uncertainty_score":0.8311067,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02982283605813952,"score_gpt":0.2874090978298213,"score_spread":0.2575862617716818,"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."}}