{"id":"W3213014003","doi":"","title":"Fighting COVID-19: Patterns in International Data, Expanded","year":2021,"lang":"en","type":"preprint","venue":"RePEc: Research Papers in Economics","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Index (typography); Geography; Population; Unemployment; Pandemic; Development economics; Human Development Index; Coronavirus disease 2019 (COVID-19); Descriptive statistics; Economic growth; Demographic economics; Economics; Demography; Human development (humanity); Statistics","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":[0.00432666,0.0003794213,0.0003951358,0.005946589,0.0004748181,0.002162179,0.0006628181,0.0004406827,0.001774745],"category_scores_gemma":[0.02578247,0.0001830204,0.0003875923,0.0131878,0.0006499676,0.001904782,0.002365279,0.0007778432,0.0007828082],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000697442,"about_ca_system_score_gemma":0.0004561248,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009508172,"about_ca_topic_score_gemma":0.008908946,"domain_scores_codex":[0.9960619,0.0008449158,0.0006591415,0.0006414122,0.001241767,0.000550902],"domain_scores_gemma":[0.9734555,0.008223759,0.009567525,0.002863108,0.005101096,0.0007889912],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001301258,0.00005082946,0.9721131,0.00009437869,0.0001072329,0.0001588149,0.001059214,0.00178012,0.0003446538,0.001362748,0.00523928,0.01755958],"study_design_scores_gemma":[0.000008330507,0.0001052706,0.9791529,0.00008021435,0.00002410857,0.00033845,0.005621037,0.003213639,0.0005406319,0.0004732214,0.01040734,0.00003486369],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9711949,0.0005147993,0.001369251,0.0006956249,0.00006362687,0.00005108932,0.01564324,0.0001176414,0.01034989],"genre_scores_gemma":[0.9792978,0.0001937767,0.001184434,0.00007179078,0.00003229128,0.00004302481,0.01833996,0.00004030621,0.0007965332],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009508172,"threshold_uncertainty_score":0.02288181,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3855369053516783,"score_gpt":0.5015492926024103,"score_spread":0.116012387250732,"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."}}