{"id":"W2317937388","doi":"10.1515/em-2014-0001","title":"Model Choice Using the Deviance Information Criterion for Latent Conditional Individual-Level Models of Infectious Disease Spread","year":2015,"lang":"en","type":"article","venue":"Epidemiologic Methods","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary; University of Guelph","funders":"","keywords":"Deviance information criterion; Deviance (statistics); Latent class model; Latent variable; Bayesian information criterion; Bayesian probability; Statistics; Latent variable model; Computer science; Missing data; Bayesian inference; Mathematics","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":[0.03686704,0.0007107693,0.001464498,0.002927586,0.0006989748,0.00190987,0.002082997,0.001313915,0.001709899],"category_scores_gemma":[0.09120091,0.0005078152,0.00159089,0.001388871,0.001740527,0.002220868,0.002573051,0.001978697,0.0001375177],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002137957,"about_ca_system_score_gemma":0.001875862,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004784463,"about_ca_topic_score_gemma":0.003143051,"domain_scores_codex":[0.9830911,0.01446225,0.0004248324,0.0008846096,0.000860688,0.0002765373],"domain_scores_gemma":[0.8959936,0.09467752,0.003832021,0.002523863,0.002260014,0.0007130404],"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":[0.0002151996,0.000111996,0.01344253,0.0001650932,0.0003877355,0.0001758894,0.0002904884,0.8351151,0.0005734783,0.1315426,0.0008084267,0.0171714],"study_design_scores_gemma":[0.0000221272,0.00008457072,0.001166423,0.00003067892,0.00002600321,0.00004105187,0.00004578732,0.9543948,0.0001852651,0.04360522,0.0003752209,0.00002288951],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1330527,0.0005053389,0.8635674,0.0006474648,0.00003340875,0.0001635053,0.0002047567,0.0001266304,0.001698745],"genre_scores_gemma":[0.8221325,0.0002736231,0.1760105,0.0001146809,0.00003917723,0.0002597075,0.0003343611,0.00005020267,0.0007852184],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.03686704,"threshold_uncertainty_score":0.1949739,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.8006097466741177,"score_gpt":0.5615392023230935,"score_spread":0.2390705443510242,"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."}}