{"id":"W2954182065","doi":"10.1016/j.envpol.2019.06.119","title":"Particulate matter transported from urban greening plants during precipitation events in Beijing, China","year":2019,"lang":"en","type":"article","venue":"Environmental Pollution","topic":"Air Quality and Health Impacts","field":"Environmental Science","cited_by":43,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"Fundamental Research Funds for the Central Universities; Beijing Municipal Education Commission; National Natural Science Foundation of China","keywords":"Beijing; Particulates; Greening; Environmental science; China; Precipitation; Urban greening; Air pollution; Environmental protection; Environmental chemistry; Geography; Ecology; Meteorology; Chemistry; Archaeology; 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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0001767154,0.0001659178,0.0001581642,0.00004188907,0.0001107497,0.000007002545,0.0001038555,0.0001034929,0.006401675],"category_scores_gemma":[0.000002801167,0.0001746127,0.00004518029,0.00006978472,0.00005184529,0.0005196724,0.00005055418,0.0001766072,0.004029549],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005194971,"about_ca_system_score_gemma":0.000003348157,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009553963,"about_ca_topic_score_gemma":0.0001805685,"domain_scores_codex":[0.9984534,0.0001029899,0.0003410765,0.0003659475,0.0003334363,0.0004031912],"domain_scores_gemma":[0.9995374,0.00001208116,0.0001222866,0.000195349,2.059067e-7,0.0001326672],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001055269,0.0001026963,0.9723334,0.000008472563,0.000006239639,0.000003092937,0.002200575,0.00426088,0.0202376,0.000001190247,0.00008856302,0.000651746],"study_design_scores_gemma":[0.0009968439,0.00004597144,0.9945663,0.00005143818,0.000007406188,0.000002409235,0.0001395279,0.00169561,0.001991316,0.0001201614,0.0001852953,0.0001977583],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9983216,0.00002878591,0.0001047069,0.0004790378,0.0001361308,0.0004155465,0.00005505289,0.00002723227,0.0004319357],"genre_scores_gemma":[0.9986047,0.00001771701,0.0001785095,0.0004617866,0.00003587439,0.00001330104,0.0001432456,0.00001926962,0.0005256619],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02223285,"threshold_uncertainty_score":0.9967459,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009456975869363437,"score_gpt":0.2245920040197092,"score_spread":0.2151350281503458,"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."}}