{"id":"W3122247502","doi":"10.22541/au.160819491.18887131/v1","title":"Shrinkage in serial intervals across cluster transmission generations of COVID-19","year":2020,"lang":"en","type":"preprint","venue":"","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Transmission (telecommunications); Cluster (spacecraft); Coronavirus disease 2019 (COVID-19); Shrinkage; Statistics; Exemplification; Inference; Competition (biology); Contact tracing; Transmission rate; Population; Demography; Computer science; Biology; Medicine; Mathematics; Environmental health; Telecommunications; Disease; Artificial intelligence; Ecology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"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":[0.008551002,0.0003565973,0.0006711351,0.001405027,0.0005394506,0.001180539,0.001125593,0.0005883474,0.00376468],"category_scores_gemma":[0.05490479,0.0003809943,0.0008436036,0.0008517271,0.00164298,0.002026005,0.001148281,0.001411997,0.0003691101],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007633741,"about_ca_system_score_gemma":0.0005940198,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005503038,"about_ca_topic_score_gemma":0.003191276,"domain_scores_codex":[0.9982601,0.0006933207,0.00008721971,0.0005805258,0.0002116035,0.0001672109],"domain_scores_gemma":[0.9724807,0.01793452,0.004324139,0.003012149,0.001586039,0.0006624718],"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":[0.0007167098,0.0001230731,0.5138679,0.0004361379,0.0006196999,0.001912448,0.003303756,0.1973161,0.006364694,0.1700091,0.008581826,0.0967486],"study_design_scores_gemma":[0.00007694853,0.0002487222,0.2456357,0.0001842795,0.0002735,0.001550838,0.001017353,0.5803924,0.002528182,0.1610892,0.00687853,0.0001243566],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8140333,0.001352326,0.1758553,0.001447314,0.0001258269,0.00008781825,0.001068862,0.0003468992,0.005682187],"genre_scores_gemma":[0.9891003,0.0003485736,0.008199178,0.0001271647,0.0000801369,0.00005150485,0.0006486804,0.00005738367,0.001387087],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008551002,"threshold_uncertainty_score":0.04522258,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.368204927682203,"score_gpt":0.49000915705242,"score_spread":0.1218042293702171,"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."}}