{"id":"W3028672976","doi":"10.1101/2020.05.24.20109215","title":"Estimating effective reproduction number using generation time versus serial interval, with application to COVID-19 in the Greater Toronto Area, Canada","year":2020,"lang":"en","type":"preprint","venue":"medRxiv","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; University of Toronto","keywords":"Mathematics; Statistics; Interval (graph theory); Coronavirus disease 2019 (COVID-19); Generation time; Standard deviation; Distribution (mathematics); Confidence interval; Combinatorics; Mathematical analysis; Medicine; Population","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001286854,0.0003746838,0.0002746109,0.001443521,0.000714142,0.0006383994,0.001058775,0.0002908208,0.0009359911],"category_scores_gemma":[0.005225896,0.0001815602,0.0004420549,0.001595871,0.0004123747,0.0002564973,0.0004063057,0.0003596278,0.00007661211],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01465628,"about_ca_system_score_gemma":0.006794738,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9685534,"about_ca_topic_score_gemma":0.9599136,"domain_scores_codex":[0.9995197,0.00009013507,0.00002550843,0.0001412229,0.0001494692,0.00007403484],"domain_scores_gemma":[0.9981212,0.0007355104,0.0002745984,0.00009998215,0.0006734956,0.0000952143],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002675551,0.000056866,0.7093638,0.000231566,0.0002676061,0.0004354032,0.0009033619,0.2427502,0.003714842,0.002786191,0.002349006,0.03687354],"study_design_scores_gemma":[0.00003118749,0.0000465393,0.4622007,0.00004377624,0.00006692667,0.0001085689,0.0007130029,0.531563,0.001908079,0.0006810153,0.002571939,0.00006522206],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9647188,0.0008657786,0.02457699,0.0001806961,0.00001399956,0.0001553778,0.005561773,0.0002536242,0.00367298],"genre_scores_gemma":[0.9869384,0.0001746831,0.01023803,0.00002302885,0.000004248224,0.00002488616,0.001833757,0.0000148913,0.000747991],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03144664,"threshold_uncertainty_score":0.1063393,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2145401578338733,"score_gpt":0.4112753786279376,"score_spread":0.1967352207940644,"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."}}