{"id":"W2616584304","doi":"10.1021/acs.est.7b01148","title":"Global Land Use Regression Model for Nitrogen Dioxide Air Pollution","year":2017,"lang":"en","type":"article","venue":"Environmental Science & Technology","topic":"Air Quality and Health Impacts","field":"Environmental Science","cited_by":246,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; Dalhousie University","funders":"NIH Office of the Director; National Institutes of Health; National Aeronautics and Space Administration","keywords":"Nitrogen dioxide; Environmental science; Air pollution; Pollution; Regression analysis; Land use; Environmental engineering; Meteorology; Geography; Chemistry; Statistics; Engineering; Mathematics; Ecology","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":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":["sts"],"category_scores_codex":[0.0005463781,0.0001902251,0.000182665,0.00008229072,0.002277571,0.00006634816,0.001117692,0.0002108035,0.0001028513],"category_scores_gemma":[0.0002389066,0.0001624799,0.00005605854,0.0002066159,0.003831418,0.001286572,0.0009686856,0.0001323068,0.0001874121],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001125886,"about_ca_system_score_gemma":0.00004214186,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002776274,"about_ca_topic_score_gemma":0.000239238,"domain_scores_codex":[0.9978812,0.00001685446,0.0002328172,0.0006297594,0.0004576631,0.0007816668],"domain_scores_gemma":[0.9986044,0.00001348533,0.0002438135,0.000877385,0.000002672123,0.0002582387],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00008074603,0.0001964213,0.9343451,0.000006273531,0.00000474589,0.000006115514,0.0001515314,0.00774182,0.02089499,0.001785311,0.001820436,0.03296651],"study_design_scores_gemma":[0.001096291,0.0003304327,0.8499679,0.00003095709,0.00002211227,0.00005075592,0.000148265,0.07091251,0.01509,0.04589341,0.015923,0.0005343489],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9755722,0.00002819346,0.01385395,0.009074295,0.0001206608,0.0004739258,0.00009931218,0.000108221,0.0006692344],"genre_scores_gemma":[0.9878048,0.00003788824,0.01039838,0.001308549,0.00002251512,0.00004247996,0.000005186243,0.00001012292,0.0003700414],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08437718,"threshold_uncertainty_score":0.9990214,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03523665640625128,"score_gpt":0.3128707701684455,"score_spread":0.2776341137621942,"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."}}