{"id":"W4308980332","doi":"10.1016/j.idm.2022.11.006","title":"Corrigendum to “Estimating effective reproduction number using generation time versus serial interval, with application to covid-19 in the Greater Toronto Area, Canada” [Infectious Disease Modelling 5 (2020) 889–896]","year":2022,"lang":"en","type":"erratum","venue":"Infectious Disease Modelling","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Coronavirus disease 2019 (COVID-19); Interval (graph theory); Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); 2019-20 coronavirus outbreak; Basic reproduction number; Statistics; Infectious disease (medical specialty); Reproduction; Demography; Mathematics; Geography; Medicine; Biology; Disease; Virology; Internal medicine; Sociology; Combinatorics; Ecology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.001933142,0.001154046,0.001262684,0.0002039573,0.001610861,0.0002704993,0.0005572948,0.0002914343,0.0002681583],"category_scores_gemma":[0.003428964,0.0009777109,0.0003253364,0.0008451464,0.00008255635,0.0003937457,0.0004555071,0.001129971,0.00002184298],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01404586,"about_ca_system_score_gemma":0.002114789,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.3642808,"about_ca_topic_score_gemma":0.2508108,"domain_scores_codex":[0.9927061,0.001215985,0.001269486,0.002525376,0.001250477,0.001032541],"domain_scores_gemma":[0.9950797,0.001106192,0.0008071861,0.001604817,0.0004048161,0.0009973097],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00134672,0.0002488139,0.0009173611,0.0004798621,0.0002341252,0.0001141095,0.001629809,0.9183465,0.0000074773,0.0001444695,0.07630686,0.0002239442],"study_design_scores_gemma":[0.0009400088,0.0002595541,0.00006816086,0.0002557806,0.0009208266,0.00002105662,0.0001825529,0.9794163,0.00000152528,0.002917701,0.01381259,0.001203909],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08679268,0.0003208005,0.8920249,0.0009384287,0.01194131,0.006623443,0.0003681448,0.0005030615,0.0004872477],"genre_scores_gemma":[0.9522712,0.0001698741,0.01084098,0.005079179,0.01316507,0.01274051,0.002057242,0.0006766042,0.002999406],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8811839,"threshold_uncertainty_score":0.9996889,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1312241259538431,"score_gpt":0.359589046747844,"score_spread":0.2283649207940009,"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."}}