{"id":"W3011112670","doi":"10.1101/2020.03.09.20033464","title":"Lessons drawn from China and South Korea for managing COVID-19 epidemic: insights from a comparative modeling study","year":2020,"lang":"en","type":"preprint","venue":"medRxiv","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Mainland China; China; Psychological intervention; Geography; Demography; Reproduction; Basic reproduction number; Coronavirus disease 2019 (COVID-19); Mainland; Confidence interval; Socioeconomics; Medicine; Statistics; Ecology; Biology; Population; Disease; Sociology; Mathematics; Infectious disease (medical specialty)","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001205163,0.0009243328,0.002948485,0.0001395688,0.0007608216,0.0001804049,0.0008794504,0.0004021849,0.00002947875],"category_scores_gemma":[0.01369165,0.0007300165,0.0003891055,0.0001688492,0.0002675477,0.00007374663,0.003202653,0.001288939,0.00001429916],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003113048,"about_ca_system_score_gemma":0.0002141734,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006017176,"about_ca_topic_score_gemma":0.001827704,"domain_scores_codex":[0.9943395,0.001080196,0.001408576,0.002203991,0.0004251622,0.0005426057],"domain_scores_gemma":[0.9848931,0.01263562,0.0008416481,0.001013451,0.0001084137,0.0005077134],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.001278555,0.001404828,0.3525269,0.002632901,0.007502522,0.0002613425,0.5575403,0.04483446,0.0003254012,0.02689037,0.004031301,0.0007710698],"study_design_scores_gemma":[0.00107929,0.00008434057,0.01156319,0.0001881041,0.0006207993,2.662967e-7,0.005842242,0.2758805,0.000008038814,0.7037992,0.0003080561,0.0006259],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6626732,0.001348125,0.3238189,0.008431131,0.000218028,0.002403592,0.0006724027,0.0003462429,0.00008835029],"genre_scores_gemma":[0.9735763,0.0001161765,0.02328909,0.001472141,0.0004628571,0.000818731,0.0001667677,0.00007828429,0.00001965274],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6769089,"threshold_uncertainty_score":0.9995151,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.551340548863109,"score_gpt":0.4833932778317901,"score_spread":0.06794727103131887,"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."}}