{"id":"W2951322845","doi":"10.1177/0962280217747054","title":"Fitting mechanistic epidemic models to data: A comparison of simple Markov chain Monte Carlo approaches","year":2018,"lang":"en","type":"article","venue":"Statistical Methods in Medical Research","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":43,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Markov chain Monte Carlo; Computer science; Markov chain; Simple (philosophy); Bayesian probability; Monte Carlo method; Approximate Bayesian computation; Data mining; Econometrics; Machine learning; Artificial intelligence; Statistics; Mathematics; Inference","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":["metaresearch"],"category_scores_codex":[0.08411132,0.0003143999,0.001725692,0.000337954,0.0002662656,0.00002785829,0.001984188,0.0003920318,0.0009063702],"category_scores_gemma":[0.6274607,0.0002392367,0.00007231683,0.001240448,0.001759603,0.00008179315,0.003981791,0.001887987,0.00002653739],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002871963,"about_ca_system_score_gemma":0.0003356521,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00151029,"about_ca_topic_score_gemma":0.001134643,"domain_scores_codex":[0.9796643,0.01263292,0.002078685,0.001349748,0.002687089,0.001587279],"domain_scores_gemma":[0.7556973,0.240996,0.0002233479,0.001591576,0.0004799791,0.001011888],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0003656327,0.0005773538,0.001270791,0.0008672319,0.00009822574,0.00005915512,0.001693986,0.00004820784,0.0001513522,0.4550656,0.02953103,0.5102714],"study_design_scores_gemma":[0.0002285014,0.0003218926,0.0002686677,0.0001579314,0.00001609622,9.877278e-7,0.0008657824,0.4937134,0.000080072,0.5034649,0.0007415359,0.0001401715],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003366892,0.0002428523,0.9897487,0.003331711,0.0001121729,0.0009439769,0.0002275752,0.00005929149,0.001966886],"genre_scores_gemma":[0.3364356,0.00002990411,0.6628709,0.0002469768,0.0001976819,0.0001372181,0.00001006914,0.00003069733,0.00004092638],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.5433493,"threshold_uncertainty_score":0.9924118,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.8933866589386059,"score_gpt":0.704013528761424,"score_spread":0.1893731301771819,"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."}}