{"id":"W4386269777","doi":"10.1016/j.scitotenv.2023.166693","title":"Long-term spatiotemporal variations in surface NO2 for Beijing reconstructed from surface data and satellite retrievals","year":2023,"lang":"en","type":"article","venue":"The Science of The Total Environment","topic":"Atmospheric and Environmental Gas Dynamics","field":"Environmental Science","cited_by":18,"is_retracted":false,"has_abstract":false,"ca_institutions":"Public Health Ontario; University of Toronto","funders":"National Natural Science Foundation of China","keywords":"Ozone Monitoring Instrument; Environmental science; Beijing; Satellite; Troposphere; Nitrogen dioxide; Hindcast; Remote sensing; Meteorology; Atmospheric sciences; Geography; Geology","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":[0.0004259394,0.0004748455,0.0003983697,0.0005469306,0.0003345241,0.0006025577,0.0004738013,0.0007155082,0.001172639],"category_scores_gemma":[0.0007286189,0.0002888239,0.0006028883,0.0009169722,0.0003964815,0.0007962184,0.0003461051,0.000289157,0.000279204],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001028126,"about_ca_system_score_gemma":0.0007467922,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06623933,"about_ca_topic_score_gemma":0.04419463,"domain_scores_codex":[0.9998658,0.00001259777,0.00001202204,0.00004907569,0.00002417444,0.00003634684],"domain_scores_gemma":[0.9997093,0.00004708198,0.0000489395,0.00005346043,0.00009855748,0.00004254493],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002376909,0.0003807515,0.5877917,0.0002929293,0.001057855,0.001294435,0.0003186809,0.2988886,0.06741816,0.001154031,0.004751552,0.03427436],"study_design_scores_gemma":[0.00006665062,0.00005230814,0.7935131,0.0000142106,0.0001678618,0.00008065303,0.0001379929,0.199052,0.005358453,0.0002274013,0.001269574,0.00005986632],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9976185,0.0001593059,0.0003426943,0.00008955129,0.00002160089,0.00000362728,0.001266417,0.00006027978,0.0004380214],"genre_scores_gemma":[0.9959194,0.00009376276,0.0002536813,0.00000987671,0.000009360146,0.000004227003,0.003266802,0.00001354639,0.0004293298],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06623933,"threshold_uncertainty_score":0.1317075,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02135269068427723,"score_gpt":0.2381244743541172,"score_spread":0.21677178366984,"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."}}