{"id":"W1167701771","doi":"10.1089/ees.2012.0236","title":"Markov Chain Monte Carlo Approach for Parameter Uncertainty Quantification and Its Impact on Groundwater Mass Transport Modeling: Influence of Prior Distribution","year":2014,"lang":"en","type":"article","venue":"Environmental Engineering Science","topic":"Groundwater flow and contamination studies","field":"Environmental Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Northern British Columbia","funders":"Program for Changjiang Scholars and Innovative Research Team in University; Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Markov chain Monte Carlo; Monte Carlo method; Hydraulic conductivity; Prior probability; Gaussian; Parametric statistics; Statistical physics; Posterior probability; Mathematics; Statistics; Computer science; Mathematical optimization; Environmental science; Soil science; Physics; Bayesian probability","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":[0.005069528,0.0008558346,0.001103505,0.001451656,0.0009019059,0.00119559,0.001250635,0.001184564,0.001386888],"category_scores_gemma":[0.0162474,0.0006942258,0.001075274,0.00132661,0.001040799,0.001978799,0.001133303,0.00157485,0.0001320356],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001445746,"about_ca_system_score_gemma":0.00278039,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02024147,"about_ca_topic_score_gemma":0.01183871,"domain_scores_codex":[0.9977126,0.001237848,0.0001115171,0.0003197567,0.0004967143,0.0001215287],"domain_scores_gemma":[0.98843,0.009783161,0.0004543708,0.000425091,0.0007928109,0.0001146029],"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":[0.00004319958,0.00002483402,0.00223971,0.00007223166,0.00005052398,0.00008347834,0.00007979731,0.9616147,0.0008830218,0.01933742,0.0002125922,0.01535857],"study_design_scores_gemma":[0.000005592177,0.00001055341,0.0002472561,0.00001237703,0.00001077918,0.00001887886,0.000007270754,0.9926886,0.0004478703,0.006343113,0.0001967742,0.00001077086],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01934942,0.0002277562,0.9792761,0.0001122162,0.00001462239,0.00004797205,0.00006143138,0.0001513048,0.0007592435],"genre_scores_gemma":[0.6582578,0.0008353299,0.3387182,0.000122072,0.0000532426,0.0004882223,0.0003704388,0.0001401971,0.001014436],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02024147,"threshold_uncertainty_score":0.04024732,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008197087067540444,"score_gpt":0.2086191907303033,"score_spread":0.2004221036627629,"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."}}