{"id":"W1968262892","doi":"10.1080/10934529.2012.707597","title":"A GIS-based multi-source and multi-box modeling approach (GMSMB) for air pollution assessment—A North American case study","year":2013,"lang":"en","type":"article","venue":"Journal of Environmental Science and Health Part A","topic":"Air Quality and Health Impacts","field":"Environmental Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Environmental science; Air quality index; Air pollution; Pollution; Geographic information system; Spatial analysis; Meteorology; Grid; Pollutant; Remote sensing; Computer science; Geography","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0005970172,0.0007242765,0.0005566712,0.0005153929,0.0006661472,0.0008623382,0.001065047,0.0009526995,0.001304248],"category_scores_gemma":[0.0006873512,0.0004395338,0.0007734848,0.0009302089,0.0003925171,0.0009196005,0.0008035516,0.000705575,0.0001729161],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001557064,"about_ca_system_score_gemma":0.001719901,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.08022982,"about_ca_topic_score_gemma":0.06815857,"domain_scores_codex":[0.9996854,0.0001546728,0.00001066426,0.0000585523,0.00006675968,0.000024007],"domain_scores_gemma":[0.999694,0.0001647077,0.00002360937,0.00002618548,0.00006918221,0.00002229079],"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":[0.00003840691,0.00008474467,0.004547994,0.00005042213,0.00003959517,0.0002825677,0.0001019929,0.9766611,0.001072815,0.003258307,0.000744373,0.0131176],"study_design_scores_gemma":[0.000008243642,0.00001751924,0.0007461569,0.000003860579,0.000007404565,0.00002050361,0.00004475868,0.997122,0.0002834542,0.0008926268,0.0008458272,0.00000767765],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5460213,0.0005977157,0.4334278,0.00129908,0.0000597545,0.0003539478,0.001714644,0.0009706576,0.015555],"genre_scores_gemma":[0.8610191,0.0003625736,0.1341562,0.00005865415,0.00001519581,0.0002826405,0.0005093702,0.00007237947,0.003523874],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08022982,"threshold_uncertainty_score":0.1595256,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.110109634910967,"score_gpt":0.3860046525068595,"score_spread":0.2758950175958925,"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."}}