{"id":"W2990457603","doi":"10.1289/isee.2013.p-1-04-21","title":"Predicting seasonal and spatial patterns of long-term nitrogen oxides concentration in Tehran, Iran using land use regression","year":2013,"lang":"en","type":"article","venue":"ISEE Conference Abstracts","topic":"Air Quality and Health Impacts","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"BC Centre for Disease Control","funders":"","keywords":"NOx; Environmental science; Nitrogen dioxide; Linear regression; Air pollution; Nitrogen oxide; Regression analysis; Atmospheric sciences; Seasonality; Metropolitan area; Meteorology; Geography; Statistics; Mathematics; Chemistry; Ecology; Biology","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":[0.0007509685,0.0006280066,0.0003170466,0.0008239666,0.0002053459,0.000516018,0.0006212551,0.0002992418,0.0005429154],"category_scores_gemma":[0.001010278,0.0001907013,0.0008741686,0.0009434822,0.0001486538,0.0003671985,0.0002750231,0.0002946482,0.0002167395],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007933052,"about_ca_system_score_gemma":0.00085356,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06426115,"about_ca_topic_score_gemma":0.05251184,"domain_scores_codex":[0.9998232,0.00003893646,0.00001461171,0.00005717873,0.00003273864,0.00003329266],"domain_scores_gemma":[0.9996092,0.0001422234,0.00007926524,0.0000268877,0.0001153658,0.00002709832],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000125964,0.0001717416,0.8620581,0.00007245166,0.0002165961,0.0002067926,0.0001575351,0.09841618,0.001889047,0.0002023594,0.001280038,0.03520318],"study_design_scores_gemma":[0.00001644348,0.00006784581,0.5564381,0.0000167813,0.00009323695,0.00005585312,0.0004624149,0.4405683,0.001065407,0.0003134724,0.0008792901,0.00002274467],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9959337,0.0001356914,0.002620756,0.00007031567,0.00001740266,0.00001275099,0.0006759894,0.0001229599,0.0004103915],"genre_scores_gemma":[0.9956631,0.000101846,0.002664593,0.000009395965,0.00001017596,0.00001802664,0.001235426,0.00001223239,0.0002852624],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06426115,"threshold_uncertainty_score":0.1277742,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08009506380175117,"score_gpt":0.3126915908188672,"score_spread":0.232596527017116,"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."}}