{"id":"W4296899493","doi":"10.1002/env.2763","title":"A dependent Bayesian Dirichlet process model for source apportionment of particle number size distribution","year":2022,"lang":"en","type":"article","venue":"Environmetrics","topic":"Air Quality and Health Impacts","field":"Environmental Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Trent University","funders":"MRC-PHE Centre for Environment and Health; NIHR Imperial Biomedical Research Centre; Medical Research Council Canada; Medical Research Council; National Institute for Health and Care Research","keywords":"Dirichlet process; Kriging; Dirichlet distribution; Covariate; A priori and a posteriori; Computer science; Econometrics; Air quality index; Bayesian probability; Environmental science; Statistics; Mathematics; Meteorology; Geography","routes":{"ca_aff":true,"ca_fund":true,"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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0009147389,0.0001225075,0.0001716909,0.00001044165,0.0003784126,0.000008953897,0.0002107211,0.00004886408,0.002244768],"category_scores_gemma":[0.0002757888,0.0001304734,0.0000699857,0.0004616265,0.00009818724,0.0001334875,0.0002185972,0.0001487863,0.00005461355],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005182404,"about_ca_system_score_gemma":0.00002511939,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000504386,"about_ca_topic_score_gemma":0.000005757205,"domain_scores_codex":[0.9981208,0.00007739342,0.0003957637,0.0003020462,0.0007140663,0.0003899005],"domain_scores_gemma":[0.9991269,0.0001793749,0.0002468523,0.0002531016,0.000003855988,0.0001899459],"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":[0.0001651439,0.00129207,0.2049621,0.00009389266,0.00001725801,0.000002088843,0.002584296,0.7685047,0.0004829415,0.0004086292,0.007173415,0.01431345],"study_design_scores_gemma":[0.001070183,0.0002907766,0.07264227,0.000003659326,0.00005373634,0.000009369712,0.000773888,0.8986645,0.002733079,0.002295622,0.02109852,0.000364351],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4934791,0.0000214389,0.5050061,0.0006591172,0.00003481544,0.0004149438,0.0001942459,0.00002116698,0.0001691169],"genre_scores_gemma":[0.9948999,0.00001588209,0.002126587,0.0007059147,0.00001450661,0.0002066373,0.00005583218,0.00001812923,0.001956627],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5028795,"threshold_uncertainty_score":0.9986673,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03053421905915329,"score_gpt":0.2936191943225253,"score_spread":0.263084975263372,"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."}}