{"id":"W2097025153","doi":"","title":"INFERENCE FOR INDIVIDUAL-LEVEL MODELS OF INFECTIOUS DISEASES IN LARGE POPULATIONS.","year":2010,"lang":"en","type":"article","venue":"PubMed","topic":"Animal Disease Management and Epidemiology","field":"Agricultural and Biological Sciences","cited_by":128,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Markov chain Monte Carlo; Inference; Computer science; Bayesian inference; Bayesian probability; Context (archaeology); Infectious disease (medical specialty); Missing data; Machine learning; Econometrics; Data mining; Artificial intelligence; Mathematics; Disease; Medicine; Geography","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":[],"consensus_categories":[],"category_scores_codex":[0.0002581931,0.00006733351,0.0001350993,0.0000216753,0.00005236834,0.00001232622,0.0001349272,0.00005656914,0.00004345144],"category_scores_gemma":[0.0003756872,0.00002881389,0.00006221304,0.0001569264,0.00003026058,0.0001272856,0.00006592751,0.00005682318,0.000001561805],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000004797064,"about_ca_system_score_gemma":0.000002623643,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001509185,"about_ca_topic_score_gemma":0.003106845,"domain_scores_codex":[0.9993231,0.00002870952,0.0001811288,0.0001490899,0.00006684098,0.0002511487],"domain_scores_gemma":[0.9996187,0.0001769686,0.00007090568,0.00003482499,0.00003071177,0.00006785586],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001977271,0.0001355346,0.8999568,0.000009160596,0.000006712241,2.317887e-7,0.00001253279,0.00003355352,0.0001177213,0.03532675,0.0003630179,0.06401819],"study_design_scores_gemma":[0.0001538356,0.00001825206,0.952024,0.000001356371,0.00001316581,7.263318e-8,0.00001753525,0.0005346679,0.000005439407,0.04664373,0.0005203692,0.00006750308],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9978023,0.00003540846,0.0001292824,0.0003371327,0.00009871315,0.0005114346,0.0003097843,0.0000290774,0.0007468296],"genre_scores_gemma":[0.9987367,0.000007522719,0.00005303077,0.0001811618,0.00007161035,0.000728872,0.0001538485,5.389304e-7,0.00006669076],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06395069,"threshold_uncertainty_score":0.1733693,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1288887857793057,"score_gpt":0.2797814636938759,"score_spread":0.1508926779145701,"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."}}