{"id":"W2507078409","doi":"10.1016/j.envpol.2016.07.027","title":"Hotspots of black carbon and PM2.5 in an urban area and relationships to traffic characteristics","year":2016,"lang":"en","type":"article","venue":"Environmental Pollution","topic":"Air Quality and Health Impacts","field":"Environmental Science","cited_by":130,"is_retracted":false,"has_abstract":false,"ca_institutions":"Dalhousie University","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico","keywords":"Environmental science; Morning; Traffic volume; Truck; Terrain; Air pollution; Rush hour; Geography; Meteorology; Physical geography; Transport engineering; Cartography; Ecology; Engineering","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":[0.0002110086,0.0001523359,0.0001878875,0.001487483,0.0004216241,0.0005137929,0.0003289974,0.0004312227,0.002438635],"category_scores_gemma":[0.0004919872,0.0002523657,0.0004231635,0.001502639,0.000339479,0.0003838446,0.0004987994,0.000174688,0.0002616998],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003241075,"about_ca_system_score_gemma":0.0002511906,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01569579,"about_ca_topic_score_gemma":0.02388572,"domain_scores_codex":[0.9997959,0.00004464153,0.00002213607,0.00005157021,0.00003028304,0.00005546364],"domain_scores_gemma":[0.9993586,0.0001433348,0.0001992888,0.00004165064,0.00009831351,0.0001587892],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001360094,0.00002999629,0.9970514,0.00001305123,0.00007004675,0.00008993709,0.0001529763,0.0002398764,0.0008117305,0.00008478107,0.0001199121,0.001200291],"study_design_scores_gemma":[0.000001815769,0.0000192166,0.9989893,0.000002123744,0.00001439897,0.0001029633,0.0003730702,0.0003219398,0.00004927596,0.00003216798,0.00009018025,0.000003489419],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9991724,0.00008406727,0.00004851082,0.00002032879,0.000001912978,0.000003210915,0.000311882,0.00000406578,0.0003534913],"genre_scores_gemma":[0.9994718,0.00004485256,0.00004926076,0.00000442999,0.00000670976,0.000004319655,0.0002312912,0.000001256578,0.0001860673],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01569579,"threshold_uncertainty_score":0.03120881,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03319871582618417,"score_gpt":0.2488586086526804,"score_spread":0.2156598928264963,"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."}}