{"id":"W2070796600","doi":"10.1371/journal.pbio.1001295","title":"How to Make Epidemiological Training Infectious","year":2012,"lang":"en","type":"article","venue":"PLoS Biology","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Fogarty International Center; Science and Technology Directorate; University of California, San Francisco; African Institute for Mathematical Sciences; Division of Mathematical Sciences; National Science Foundation; National Institutes of Health; Center for Discrete Mathematics and Theoretical Computer Science; National Institute of General Medical Sciences; U.S. Department of Homeland Security","keywords":"Epidemiology; Infectious disease (medical specialty); Variety (cybernetics); Computer science; Medical education; Management science; Data science; Engineering ethics; Medicine; Mathematics education; Disease; Psychology; Artificial intelligence; Pathology; Engineering","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.001772493,0.0002731377,0.0009129329,0.00007538572,0.0001512626,0.00001042921,0.0002439189,0.0003349214,0.0001257094],"category_scores_gemma":[0.06184766,0.0001739793,0.0001620321,0.0001910528,0.0001798908,0.00004495076,0.0003471562,0.0003149757,0.0001703614],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001006547,"about_ca_system_score_gemma":0.00001261789,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001842995,"about_ca_topic_score_gemma":0.00002935932,"domain_scores_codex":[0.9973863,0.0007288361,0.0004087857,0.0004155039,0.0000755493,0.0009850746],"domain_scores_gemma":[0.9888087,0.01039208,0.0001530587,0.0003206802,0.00005321869,0.0002722807],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00004431847,0.0004360968,0.6129499,0.00007168929,0.0002802302,0.000005669412,0.001435926,0.000003496407,0.005743568,0.3474873,0.0098982,0.02164359],"study_design_scores_gemma":[0.0006038478,0.001051259,0.1015793,0.00004359465,0.0001307379,0.00004137692,0.0004971928,0.00008349952,0.0005092095,0.6377202,0.256845,0.0008947929],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9435365,0.000523094,0.02545701,0.0231656,0.0005246509,0.0006215182,0.0000210339,0.0006386169,0.005511929],"genre_scores_gemma":[0.9773272,0.00003765189,0.01442993,0.006967771,0.0007785796,0.0001764224,0.000005159691,0.00001826597,0.0002590283],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5113706,"threshold_uncertainty_score":0.9460548,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5003522986155139,"score_gpt":0.4486555376314671,"score_spread":0.05169676098404674,"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."}}