{"id":"W2148994127","doi":"10.1111/risa.12006","title":"Harnessing the Theoretical Foundations of the Exponential and Beta‐Poisson Dose‐Response Models to Quantify Parameter Uncertainty Using Markov Chain Monte Carlo","year":2013,"lang":"en","type":"article","venue":"Risk Analysis","topic":"Fecal contamination and water quality","field":"Environmental Science","cited_by":52,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada; Public Health Agency of Canada","funders":"","keywords":"Poisson distribution; Markov chain Monte Carlo; Bayes' theorem; Mathematics; Beta distribution; Bayesian probability; Applied mathematics; Statistics; Monte Carlo method; BETA (programming language); Markov chain; Statistical physics; Computer science; Physics","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":[0.0012746,0.0001185205,0.000203098,0.00006560834,0.0003696374,0.0001180251,0.0002362009,0.00005057668,0.000622735],"category_scores_gemma":[0.0001595855,0.00006525094,0.0001952701,0.0006474552,0.0004704658,0.0001881981,0.0002226672,0.0001165798,0.00001664245],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007816731,"about_ca_system_score_gemma":0.000008540763,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01328125,"about_ca_topic_score_gemma":0.0008408408,"domain_scores_codex":[0.9978234,0.00109896,0.0002859593,0.0002646142,0.0003384367,0.000188604],"domain_scores_gemma":[0.9989222,0.0004222686,0.0001337685,0.0004105146,0.00002781551,0.00008337571],"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":[0.0004137968,0.0002241941,0.2927302,0.00001279725,0.000864913,0.000001487,0.01612422,0.6348031,0.01765711,0.005721788,0.0001472851,0.03129914],"study_design_scores_gemma":[0.00009809371,0.00001038733,0.2961344,0.000004155445,0.0004007572,3.703344e-7,0.0003877087,0.7005165,0.0004056753,0.001932549,0.00002572383,0.00008373085],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9674945,0.00001299683,0.03037421,0.001756945,0.00002980188,0.0002376515,0.00001630124,0.00000875632,0.0000688577],"genre_scores_gemma":[0.9987052,0.000005300756,0.0009592071,0.0001692295,0.000009202737,0.0000167265,0.000001861386,0.000006564127,0.0001267169],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06571341,"threshold_uncertainty_score":0.9932894,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02896444415833714,"score_gpt":0.277101930240107,"score_spread":0.2481374860817699,"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."}}