{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001095168,0.001013215,0.0007011146,0.000905919,0.0008936645,0.001757462,0.00245678,0.0008829146,0.003548555],"category_scores_gemma":[0.002524319,0.0006559929,0.0007208902,0.0009767397,0.0005047623,0.0004482932,0.0007591188,0.001033856,0.0008340981],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.009984814,"about_ca_system_score_gemma":0.008630435,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9375561,"about_ca_topic_score_gemma":0.8624681,"domain_scores_codex":[0.999526,0.0001088427,0.00001769998,0.0001414581,0.00007295517,0.0001329593],"domain_scores_gemma":[0.9987314,0.0005051898,0.00009877322,0.00003447674,0.0005605001,0.00006952773],"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":[0.0001542863,0.00009952036,0.06188029,0.00006906084,0.0001019757,0.0002928574,0.0001414158,0.9083159,0.0008968679,0.002564613,0.003071585,0.02241169],"study_design_scores_gemma":[0.000008139256,0.000008521062,0.003950445,0.000006495286,0.000008793495,0.00001024229,0.00003731091,0.994952,0.00007466121,0.0002302958,0.0007060522,0.00000698005],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8693468,0.0008999737,0.1109442,0.001123403,0.00006490658,0.0002779639,0.006397076,0.0008837474,0.01006197],"genre_scores_gemma":[0.9619983,0.0003470346,0.01914239,0.00008544904,0.00001453259,0.0002127606,0.003719616,0.0001063856,0.0143735],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06244391,"threshold_uncertainty_score":0.1256233,"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."}}