{"id":"W2177199724","doi":"10.5194/acp-16-4693-2016","title":"Quantification of black carbon mixing state from traffic: implications for aerosol optical properties","year":2016,"lang":"en","type":"article","venue":"Atmospheric chemistry and physics","topic":"Atmospheric chemistry and aerosols","field":"Earth and Planetary Sciences","cited_by":64,"is_retracted":false,"has_abstract":true,"ca_institutions":"Occupational Cancer Research Centre; Ministry of the Environment, Conservation and Parks; University of Toronto","funders":"FP7 People: Marie-Curie Actions; Natural Sciences and Engineering Research Council of Canada; Department of Energy, Labor and Economic Growth; U.S. Department of Energy","keywords":"Aerosol; Single-scattering albedo; Carbon black; Particle (ecology); Soot; Atmosphere (unit); Chemistry; Mass concentration (chemistry); Atmospheric sciences; Meteorology; Combustion; Physics; Geology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0004359764,0.0004113429,0.0001762907,0.0005561615,0.000234078,0.0005479847,0.0002224265,0.0003850932,0.0003309812],"category_scores_gemma":[0.0008030805,0.0001514862,0.0002129503,0.000377291,0.0001644637,0.0004713923,0.0002095569,0.000269188,0.0001270924],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004121841,"about_ca_system_score_gemma":0.0002968257,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004810337,"about_ca_topic_score_gemma":0.00446154,"domain_scores_codex":[0.9998579,0.00002573915,0.000005455116,0.00003947722,0.00005581086,0.00001562869],"domain_scores_gemma":[0.9996982,0.0001114836,0.00005938229,0.00002957667,0.00008159522,0.00001984923],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0005530176,0.0003399184,0.3959531,0.0001512669,0.0002173366,0.0001054851,0.0001011239,0.04205487,0.5137439,0.0008342071,0.0003160434,0.04562979],"study_design_scores_gemma":[0.00001467316,0.0002225256,0.308576,0.00001513689,0.00009883019,0.0001296569,0.00006800387,0.4328043,0.2561163,0.001147535,0.0007604656,0.00004651205],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9872649,0.0001213828,0.01116048,0.00002962023,0.00001100865,0.00001800848,0.0004104218,0.0001063376,0.0008777883],"genre_scores_gemma":[0.9959161,0.00005227563,0.003538607,0.000008796989,0.000004455547,0.000009113237,0.000322403,0.000009294077,0.000138966],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004810337,"threshold_uncertainty_score":0.009564698,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02029567010471912,"score_gpt":0.2151457052448491,"score_spread":0.19485003514013,"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."}}