{"id":"W2990130261","doi":"10.1289/isee.2011.00980","title":"DEVELOPING A LAND USE REGRESSION MODEL FOR ULTRAFINE PARTICLE CONCENTRATIONS IN VANCOUVER, CANADA","year":2011,"lang":"en","type":"article","venue":"ISEE Conference Abstracts","topic":"Air Quality and Health Impacts","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University; University of British Columbia","funders":"","keywords":"Ultrafine particle; Environmental science; Particle number; Population density; Linear regression; Air pollution; Population; Range (aeronautics); Spatial variability; Atmospheric sciences; Statistics; Meteorology; Geography; Environmental health; Medicine; Mathematics; Engineering; Chemistry","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000204932,0.0001121531,0.0001340319,0.0000112749,0.0001216437,0.00002226474,0.00012903,0.00006146669,0.0002296925],"category_scores_gemma":[0.0002067383,0.00009975237,0.00001520596,0.00008429289,0.00006028077,0.0004550971,0.00002811431,0.0001034357,0.00001506521],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002689189,"about_ca_system_score_gemma":0.0006149949,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.3453913,"about_ca_topic_score_gemma":0.9663512,"domain_scores_codex":[0.998862,0.00002482976,0.000316839,0.0002152567,0.0001741439,0.0004069492],"domain_scores_gemma":[0.9994065,0.0001121875,0.0001096812,0.0001521596,0.00002241337,0.000197057],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0007488669,0.0007614192,0.4394471,0.0002500983,0.00002978332,0.0001080348,0.02608136,0.406205,0.004326793,0.01225123,0.08984576,0.0199446],"study_design_scores_gemma":[0.001018239,0.00005330127,0.9095517,0.0001392922,0.000008492832,0.000001965215,0.0003277161,0.07356673,0.006256258,0.003058557,0.005672229,0.0003455097],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9909494,0.000007838476,0.006338601,0.001041824,0.0001557416,0.0003539875,0.00003026706,0.00002147029,0.001100903],"genre_scores_gemma":[0.9923102,0.00002183274,0.005613877,0.001501354,0.000009678988,0.00003047489,0.000008185435,0.000007240542,0.0004972002],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6209599,"threshold_uncertainty_score":0.6589678,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1961886124802759,"score_gpt":0.3169156747183477,"score_spread":0.1207270622380718,"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."}}