{"id":"W2986334865","doi":"10.1073/pnas.1916910116","title":"The DAGs of war","year":2019,"lang":"en","type":"letter","venue":"Proceedings of the National Academy of Sciences","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Development economics; Context (archaeology); Outbreak; Famine; Public health; Disease; Pandemic; Political science; Criminology; Economic growth; Geography; Environmental health; Medicine; Infectious disease (medical specialty); Virology; Sociology; Law; Coronavirus disease 2019 (COVID-19)","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":[0.0033259,0.0003507772,0.0006394117,0.0015169,0.002720429,0.003668033,0.0007171616,0.003637548,0.02963674],"category_scores_gemma":[0.03844259,0.000400209,0.0005242891,0.001612859,0.004197709,0.00745608,0.002343176,0.006109966,0.003339552],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003104391,"about_ca_system_score_gemma":0.001350251,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005382522,"about_ca_topic_score_gemma":0.007310602,"domain_scores_codex":[0.9986405,0.0007939544,0.0000492581,0.0002016303,0.0001460083,0.0001687176],"domain_scores_gemma":[0.9889765,0.007834983,0.0007478671,0.001107327,0.0005729998,0.0007603905],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001424632,0.00001876289,0.004281797,0.00006183201,0.00005014343,0.0002156516,0.0004796756,0.001072043,0.00005084563,0.7139501,0.2443363,0.03534041],"study_design_scores_gemma":[0.00003688208,0.000007595103,0.001979528,0.0000565071,0.00001158378,0.0001809511,0.0002563735,0.0009954178,0.00002888108,0.9460977,0.05033552,0.00001320574],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.01960761,0.003794135,0.007129075,0.8703545,0.003374539,0.00002186697,0.002050007,0.0001521262,0.09351615],"genre_scores_gemma":[0.8495535,0.007397838,0.002283691,0.1036484,0.008844904,0.0001102474,0.0009562681,0.0001595385,0.02704555],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.02963674,"threshold_uncertainty_score":0.09914476,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2853488785308276,"score_gpt":0.4307070741580569,"score_spread":0.1453581956272293,"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."}}