{"id":"W2040625593","doi":"10.1038/srep08751","title":"Modeling Post-death Transmission of Ebola: Challenges for Inference and Opportunities for Control","year":2015,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Viral Infections and Outbreaks Research","field":"Medicine","cited_by":115,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Centers for Disease Control and Prevention; Burroughs Wellcome Fund","keywords":"Transmission (telecommunications); Mathematical modelling of infectious disease; Basic reproduction number; Inference; Identifiability; Computer science; Disease; Ebola virus; Medicine; Infectious disease (medical specialty); Artificial intelligence; Environmental health; Population; Machine learning; Telecommunications","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":[0.02915727,0.001201283,0.002722224,0.001487904,0.001290763,0.003772002,0.004240763,0.003661587,0.001156062],"category_scores_gemma":[0.1038455,0.001382386,0.001550379,0.001331869,0.003980882,0.00841827,0.003195297,0.005287545,0.0002123258],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002160785,"about_ca_system_score_gemma":0.003377574,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02270292,"about_ca_topic_score_gemma":0.01150997,"domain_scores_codex":[0.9918384,0.005809076,0.0004071096,0.0009453781,0.0006803487,0.000319711],"domain_scores_gemma":[0.8936155,0.09577574,0.005217344,0.003208923,0.001616015,0.0005664863],"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":[0.00006498556,0.00014134,0.01592541,0.000243136,0.0002375249,0.0002169702,0.0005093121,0.7694475,0.0002925356,0.1687774,0.001420503,0.04272348],"study_design_scores_gemma":[0.00002005434,0.00002922744,0.001169797,0.00006213373,0.00002341094,0.00003527783,0.0001319067,0.7811249,0.00009748476,0.2163973,0.0008808858,0.00002751273],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03773433,0.002735154,0.9390436,0.01769266,0.0001387914,0.0001145257,0.0002830003,0.000176561,0.002081313],"genre_scores_gemma":[0.7865174,0.005038772,0.2038576,0.001514551,0.0006805616,0.0004368036,0.0003805216,0.00007458123,0.001499242],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02915727,"threshold_uncertainty_score":0.1542002,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2470390784341014,"score_gpt":0.381747491645724,"score_spread":0.1347084132116226,"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."}}