{"id":"W2980259060","doi":"10.5539/enrr.v9n4p9","title":"Tropospheric NO2 Monitoring Using the Multi-Axis Differential Optical Absorption Spectroscopy in Urban Area","year":2019,"lang":"en","type":"article","venue":"Environment and Natural Resources Research","topic":"Air Quality Monitoring and Forecasting","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro","keywords":"Differential optical absorption spectroscopy; Trace gas; Troposphere; Environmental science; Absorption (acoustics); Atmospheric sciences; Remote sensing; Atmosphere (unit); Absorption spectroscopy; Ultraviolet; Meteorology; Optics; Physics; Geology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007555317,0.0001709099,0.0001654417,0.00005344518,0.0003418535,0.0001029584,0.0002835983,0.00009736013,0.0005202212],"category_scores_gemma":[0.00005190091,0.00011927,0.00005178974,0.0002183867,0.000418336,0.000153191,0.0005124508,0.0008565398,0.0001529404],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000437477,"about_ca_system_score_gemma":0.00000382906,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003501485,"about_ca_topic_score_gemma":0.000008001909,"domain_scores_codex":[0.9975935,0.0002280448,0.0002256628,0.0004583507,0.0008768403,0.0006176371],"domain_scores_gemma":[0.9993352,0.0002325556,0.00004750741,0.0002713082,0.000003206799,0.0001102038],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001010953,0.00007829902,0.8044576,0.00001060707,0.000008363152,0.000005953038,0.00168691,0.001398277,0.1875931,0.000007274414,0.00002109153,0.004631383],"study_design_scores_gemma":[0.0006855431,0.0001682161,0.9196702,0.00006092897,0.000007416875,0.000005258082,0.001428111,0.06682086,0.008285156,0.00003642822,0.002579994,0.0002519113],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9984685,0.0004512127,0.0001583317,0.0001146089,0.0001592687,0.0003194291,9.00137e-7,0.00001564076,0.0003121329],"genre_scores_gemma":[0.9961682,0.0001295357,0.001509865,0.000004434323,0.0001994006,0.00001138636,0.000001857966,0.00001917655,0.001956168],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.179308,"threshold_uncertainty_score":0.5696058,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05035212089072862,"score_gpt":0.3177172810009825,"score_spread":0.2673651601102539,"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."}}