{"id":"W2565038447","doi":"10.1016/j.epidem.2016.12.001","title":"Defining epidemics in computer simulation models: How do definitions influence conclusions?","year":2016,"lang":"en","type":"article","venue":"Epidemics","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":52,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Government of Canada; University of Missouri","keywords":"Public health interventions; Cutoff; Computer science; Epidemic model; Public health; Population; Infectious disease (medical specialty); Disease; Data science; Psychological intervention; Econometrics; Operations research; Risk analysis (engineering); Medicine; Environmental health; Pathology; Engineering; Mathematics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.3133414,0.003304525,0.007356408,0.01220351,0.003951809,0.0281903,0.009546135,0.006802108,0.00312448],"category_scores_gemma":[0.6743298,0.002823934,0.003348512,0.01315673,0.02500391,0.04982673,0.0120323,0.02053077,0.0008714969],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0105986,"about_ca_system_score_gemma":0.008426493,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008360229,"about_ca_topic_score_gemma":0.005509107,"domain_scores_codex":[0.5690802,0.3858471,0.01377159,0.0102893,0.01864962,0.002362164],"domain_scores_gemma":[0.2328045,0.6938958,0.01870225,0.02912,0.02299899,0.002478398],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001733508,0.0001541794,0.01126365,0.002323607,0.00106025,0.0001725357,0.006447209,0.03103278,0.0002185873,0.8855349,0.01005204,0.05156695],"study_design_scores_gemma":[0.00007270625,0.00009683047,0.001571299,0.002957454,0.0001767329,0.0001238779,0.002783819,0.04157001,0.0004872603,0.929258,0.02073866,0.0001634045],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02516112,0.04311579,0.8112651,0.09661207,0.005074041,0.0005030805,0.0006442607,0.0006834465,0.01694112],"genre_scores_gemma":[0.4799076,0.02843069,0.4623487,0.01922427,0.003959885,0.002479562,0.0007283423,0.001601764,0.001319078],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6866586,"threshold_uncertainty_score":0.8467723,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3454422967654884,"score_gpt":0.4195550114551164,"score_spread":0.07411271468962799,"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."}}