{"id":"W4394184452","doi":"10.6084/m9.figshare.14331311","title":"Metadata record for: 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":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Metadata; Pandemic; Coronavirus disease 2019 (COVID-19); Database; 2019-20 coronavirus outbreak; Epidemiology; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); National database; Computer science; World Wide Web; Data science; Virology; Medicine; Outbreak","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":[0.000829426,0.00151482,0.001221724,0.004688495,0.002403865,0.003186195,0.002942929,0.001297123,0.08481249],"category_scores_gemma":[0.008584235,0.0007199624,0.0009908202,0.01325235,0.0007130291,0.001284183,0.001580639,0.001660806,0.0477137],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02246264,"about_ca_system_score_gemma":0.0496792,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9576264,"about_ca_topic_score_gemma":0.967127,"domain_scores_codex":[0.9988681,0.00006722621,0.0001079938,0.0002196352,0.0004587686,0.0002782405],"domain_scores_gemma":[0.992732,0.0008033053,0.0003545345,0.0006795895,0.004492014,0.0009384897],"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":[0.00005353735,0.00001091638,0.00172813,0.0003304327,0.00001941892,0.00002233528,0.0000305888,0.0003684799,0.00007980915,0.0005830927,0.9944136,0.00235967],"study_design_scores_gemma":[0.0001780074,0.00001272594,0.02061362,0.0005532603,0.00004798859,0.00005412541,0.0002792363,0.001046412,0.0006862017,0.001025833,0.9754187,0.00008387354],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00007519632,0.00001716071,0.00003589587,0.00005132327,0.000009689006,0.00001148289,0.9989637,0.000141553,0.0006940638],"genre_scores_gemma":[0.0007486623,0.00005924131,0.0002654219,0.0000480807,0.000004344087,0.00003645267,0.9978472,0.00006260718,0.0009280153],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.08481249,"threshold_uncertainty_score":0.283726,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1524274087039737,"score_gpt":0.3718312255492612,"score_spread":0.2194038168452875,"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."}}