{"id":"W4394615219","doi":"10.1515/ijb-2023-0102","title":"Ensemble learning methods of inference for spatially stratified infectious disease systems","year":2024,"lang":"en","type":"article","venue":"The International Journal of Biostatistics","topic":"Animal Disease Management and Epidemiology","field":"Agricultural and Biological Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Winnipeg; University of Calgary","funders":"","keywords":"Markov chain Monte Carlo; Covariate; Computer science; Bayesian inference; Inference; Bayesian probability; Ensemble learning; Population; Boosting (machine learning); Random forest; Machine learning; Markov chain; Artificial intelligence; Statistical inference; Bayes' theorem; Statistics; Econometrics; Mathematics","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":[0.0007915793,0.00006495252,0.000128734,0.00002335162,0.00004727537,0.0000848356,0.0002651239,0.00002274129,0.00005367463],"category_scores_gemma":[0.001127789,0.00002295935,0.00009243705,0.00006635869,0.00004851583,0.0000617627,0.00004242558,0.00009576084,0.000002812557],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002086731,"about_ca_system_score_gemma":0.00003031673,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005890572,"about_ca_topic_score_gemma":0.00002010455,"domain_scores_codex":[0.9991678,0.0001447491,0.0003510762,0.00007292768,0.0001795432,0.00008389931],"domain_scores_gemma":[0.9971831,0.002207762,0.0002353292,0.00001918775,0.000305733,0.00004882996],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007431919,0.0001353477,0.007292328,0.0001608007,0.0006691972,0.00007678827,0.0002762095,0.005970299,0.08099093,0.3236646,0.0072314,0.5727889],"study_design_scores_gemma":[0.0009728674,0.004339579,0.2045919,0.001154994,0.001061844,0.0000884883,0.001825436,0.3616855,0.002033859,0.23234,0.189162,0.0007434451],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5401402,0.002343371,0.4473595,0.005114776,0.003272569,0.0003888829,0.00038234,0.0000429475,0.0009554843],"genre_scores_gemma":[0.9975589,0.0001974447,0.00158982,0.00008045541,0.0004064096,0.000003490999,0.00002790554,9.469125e-7,0.0001346307],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5720454,"threshold_uncertainty_score":0.135015,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04953226175499779,"score_gpt":0.3602157903403812,"score_spread":0.3106835285853834,"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."}}