{"id":"W4394363374","doi":"10.6084/m9.figshare.13480383","title":"A sub-national real-time epidemiological and vaccination database for the COVID-19 pandemic in Canada","year":2021,"lang":"en","type":"dataset","venue":"Figshare","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Pandemic; Coronavirus disease 2019 (COVID-19); Epidemiology; 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); National database; Vaccination; Virology; Database; Computer science; Data science; Medicine; Outbreak; Disease; Pathology; Infectious disease (medical specialty)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008497731,0.001252061,0.001318161,0.003484487,0.001645044,0.001757681,0.002852367,0.001065051,0.0364215],"category_scores_gemma":[0.007706294,0.000774289,0.001210731,0.01171851,0.0003440506,0.000643191,0.001153273,0.001659246,0.01028582],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01874875,"about_ca_system_score_gemma":0.04529826,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.983525,"about_ca_topic_score_gemma":0.9862367,"domain_scores_codex":[0.9991058,0.00006407094,0.0001193879,0.0001902624,0.0002987285,0.0002218241],"domain_scores_gemma":[0.9941202,0.0005319066,0.00046216,0.0003273569,0.003966439,0.0005918316],"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":[0.0001386203,0.00002482412,0.008067605,0.0008853322,0.0001293208,0.00003937142,0.00004776926,0.0007088124,0.00004790243,0.0006882245,0.9847614,0.004460918],"study_design_scores_gemma":[0.000896928,0.00004392886,0.1832807,0.003267518,0.0004096633,0.0001892129,0.0004924791,0.00315517,0.0004686951,0.001351732,0.806279,0.0001649487],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0002022673,0.00006540303,0.00002846658,0.000044091,0.00000706381,0.00001375684,0.9993051,0.00002737208,0.0003065333],"genre_scores_gemma":[0.002956139,0.0002128049,0.0004208432,0.00008510099,0.000006018875,0.000104224,0.9952666,0.0000286405,0.0009197119],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.0364215,"threshold_uncertainty_score":0.1360323,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4298374537046615,"score_gpt":0.4484931759511311,"score_spread":0.01865572224646955,"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."}}