{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006566495,0.001632156,0.003073337,0.002510711,0.001506382,0.003338946,0.005705902,0.00436463,0.0118295],"category_scores_gemma":[0.01744987,0.001539172,0.003297026,0.003253679,0.003049723,0.004193808,0.002562763,0.004202022,0.002761424],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002682213,"about_ca_system_score_gemma":0.001702377,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01704182,"about_ca_topic_score_gemma":0.01317776,"domain_scores_codex":[0.9960898,0.00190128,0.0002017386,0.001055446,0.000407711,0.0003439701],"domain_scores_gemma":[0.9896013,0.00836003,0.0006251421,0.0004973008,0.0006497562,0.0002664371],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004419022,0.0001507796,0.005352379,0.000388572,0.0002156203,0.0007763836,0.001240486,0.4276942,0.001838963,0.5160369,0.008459149,0.03740467],"study_design_scores_gemma":[0.0001118956,0.00004868371,0.001008744,0.00007142713,0.00008325455,0.0002423175,0.000111127,0.8091564,0.0003976068,0.1822503,0.00643682,0.00008149381],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02276807,0.001452284,0.9647273,0.001719187,0.0002961943,0.0002379325,0.002142715,0.0004778276,0.006178377],"genre_scores_gemma":[0.6663872,0.004407784,0.2626468,0.001148586,0.001075954,0.002636899,0.006167306,0.0006221097,0.05490737],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01704182,"threshold_uncertainty_score":0.03957361,"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."}}