{"id":"W4361282434","doi":"10.1021/acs.estlett.3c00084","title":"Critical Role of Secondary Organic Aerosol in Urban Atmospheric Visibility Improvement Identified by Machine Learning","year":2023,"lang":"en","type":"article","venue":"Environmental Science & Technology Letters","topic":"Air Quality and Health Impacts","field":"Environmental Science","cited_by":47,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Ministry of Science and Technology of the People's Republic of China; Science and Technology Foundation of Shenzhen City","keywords":"Visibility; Aerosol; Environmental science; Meteorology; Radiative transfer; Atmospheric sciences; Radiative forcing; Computer science; Geography; Physics; Optics","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":[0.001064407,0.0003744913,0.0002393785,0.0006749887,0.00028037,0.0007597284,0.0003666853,0.0004730077,0.0005156877],"category_scores_gemma":[0.001680409,0.0001822983,0.0007103924,0.000416982,0.0003613397,0.0008041644,0.0006286962,0.0004419876,0.00008864258],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005160226,"about_ca_system_score_gemma":0.0007639243,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01112021,"about_ca_topic_score_gemma":0.01117684,"domain_scores_codex":[0.9996878,0.00008761123,0.00001294338,0.00007562954,0.00005633855,0.00007965721],"domain_scores_gemma":[0.9992329,0.0002915882,0.0001681159,0.00007000299,0.0001662726,0.00007118803],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002610713,0.0002352493,0.6443042,0.0001437286,0.0003668587,0.0004518658,0.0002468673,0.2629806,0.01752099,0.00496731,0.001235717,0.06728558],"study_design_scores_gemma":[0.000007479687,0.000050325,0.1609999,0.00001138317,0.00006327882,0.00004210541,0.0001156683,0.832992,0.002388147,0.002601805,0.0007130309,0.00001490973],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9798306,0.0003313126,0.0180934,0.0003724644,0.00002514302,0.00001544401,0.0001384596,0.0001106298,0.001082522],"genre_scores_gemma":[0.9985238,0.00004343724,0.001239401,0.00001229862,0.000007481124,0.00000179312,0.0000686833,0.000004823645,0.00009837069],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01112021,"threshold_uncertainty_score":0.02211094,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005430302804308657,"score_gpt":0.2395411706606795,"score_spread":0.2341108678563709,"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."}}