{"id":"W2148537287","doi":"10.1111/j.1539-6924.2005.00591.x","title":"Development of the PEARLS Model (Particulate Exposure from Ambient to Regional Lung by Subgroup) and Use of Monte Carlo Simulation to Predict Internal Exposure to PM<sub>2.5</sub> in Toronto<sup>1</sup>","year":2005,"lang":"en","type":"article","venue":"Risk Analysis","topic":"Air Quality and Health Impacts","field":"Environmental Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Aerodynamic diameter; Particulates; Population; Demography; Monte Carlo method; Subgroup analysis; Exposure assessment; Environmental health; Medicine; Environmental science; Air pollution; Statistics; Atmospheric sciences; Mathematics; Chemistry; Physics; Confidence interval","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0005291419,0.0006617435,0.0004969121,0.0004409069,0.0004929926,0.0006154032,0.001467742,0.0006756297,0.002110976],"category_scores_gemma":[0.002057531,0.0004755507,0.0008739667,0.0004455651,0.0003596334,0.0003930081,0.0004941713,0.0005321727,0.0002180981],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002878211,"about_ca_system_score_gemma":0.003315677,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.4392439,"about_ca_topic_score_gemma":0.2821362,"domain_scores_codex":[0.9997564,0.00007376423,0.00001237882,0.0000490242,0.00006266119,0.00004576074],"domain_scores_gemma":[0.999459,0.0002808998,0.00004571533,0.00001912145,0.0001569586,0.00003834181],"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":[0.00001656726,0.000006747975,0.003668192,0.00001053489,0.00001426327,0.0000434187,0.00002990237,0.9918804,0.0001813004,0.001716481,0.0003092157,0.002123022],"study_design_scores_gemma":[0.000005439505,0.00001013846,0.000673886,0.000002309665,0.000009445049,0.000008302683,0.0000142644,0.9983733,0.00009066334,0.0005382592,0.0002695314,0.000004360747],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4905473,0.0004824133,0.4870411,0.0009823312,0.00008377212,0.0003623945,0.004090466,0.001078319,0.01533197],"genre_scores_gemma":[0.9543971,0.0002633577,0.03805849,0.0000951545,0.0000256808,0.0002994202,0.001383999,0.00009168268,0.005385015],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5607561,"threshold_uncertainty_score":0.8733745,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02233876621135268,"score_gpt":0.2697580588594074,"score_spread":0.2474192926480547,"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."}}