{"id":"W3154958875","doi":"10.1108/eemcs-05-2020-0161","title":"Back to basics: understanding the numbers behind COVID-19","year":2021,"lang":"en","type":"article","venue":"Emerald Emerging Markets Case Studies","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Timeline; Pandemic; Descriptive statistics; Government (linguistics); Credibility; Coronavirus disease 2019 (COVID-19); Quarter (Canadian coin); Political science; Geography; Economic growth; Statistics; Medicine; Economics; Law","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":[0.01383117,0.001176433,0.0007420906,0.002977118,0.004103893,0.01177441,0.002707908,0.005795365,0.05584688],"category_scores_gemma":[0.07026571,0.000618546,0.0009150926,0.001615055,0.01031844,0.03947758,0.005959164,0.01496092,0.01698171],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007109638,"about_ca_system_score_gemma":0.008809922,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0144635,"about_ca_topic_score_gemma":0.01027098,"domain_scores_codex":[0.9933591,0.003489183,0.0003627815,0.0005351051,0.00155689,0.0006969262],"domain_scores_gemma":[0.9770189,0.01172837,0.001186803,0.0007945867,0.00676846,0.00250288],"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":[0.0000802693,0.00008249398,0.003209295,0.001040023,0.00002002019,0.0004035884,0.02057873,0.0003518261,0.0003159083,0.1514768,0.6512309,0.1712102],"study_design_scores_gemma":[0.000009321004,0.00005966332,0.001626275,0.004005859,0.000008929801,0.0003865802,0.02432488,0.0003027952,0.0002349699,0.2233702,0.7456154,0.00005515309],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.004435091,0.0260876,0.02915754,0.8410091,0.02014509,0.0003048998,0.001617629,0.0005192393,0.07672379],"genre_scores_gemma":[0.2265265,0.1079518,0.06666778,0.4024382,0.02754729,0.001418036,0.004206629,0.001836461,0.1614075],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.05584688,"threshold_uncertainty_score":0.1868265,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3883123194837158,"score_gpt":0.4697283995464591,"score_spread":0.08141608006274331,"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."}}