{"id":"W4311616527","doi":"10.5194/gmd-2022-292","title":"Can TROPOMI-NO <sub>2</sub> satellite data be used to track the drop and resurgence of NO <sub>x</sub> emissions between 2019–2021 using the multi-source plume method (MSPM)?","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Air Quality and Health Impacts","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada","funders":"Umweltbundesamt","keywords":"NOx; Environmental science; Emission inventory; Meteorology; Satellite; Air pollution; Air quality index; Combustion; Engineering; Geography","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":[0.0005906752,0.0003346702,0.0002284831,0.000702703,0.0001235499,0.0007687177,0.000338716,0.0004711185,0.0009286573],"category_scores_gemma":[0.001045581,0.0001086953,0.0004005154,0.001089554,0.0001690814,0.0007421845,0.0003068495,0.0002509335,0.0004375688],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005495473,"about_ca_system_score_gemma":0.0004078343,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03942868,"about_ca_topic_score_gemma":0.05681154,"domain_scores_codex":[0.9998466,0.00003128802,0.00001077014,0.00004214771,0.00003839122,0.00003086372],"domain_scores_gemma":[0.9997056,0.00003923058,0.0001014397,0.00004216918,0.00008715346,0.00002436825],"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":[0.0006024785,0.0001821122,0.8484542,0.0003990496,0.0005165864,0.0003247011,0.0001845807,0.047196,0.01892954,0.001375446,0.007347408,0.07448797],"study_design_scores_gemma":[0.00004892565,0.0001088033,0.8599702,0.0001320246,0.0001720495,0.00007460669,0.0004595613,0.1106714,0.01199675,0.0005302931,0.01577115,0.00006428917],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9648807,0.0009941624,0.006928194,0.0007650881,0.0001408647,0.00007502067,0.01938437,0.0005420442,0.006289427],"genre_scores_gemma":[0.9786159,0.00040189,0.008272251,0.00008308762,0.00002213768,0.00004110983,0.01163944,0.00004264489,0.0008815307],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03942868,"threshold_uncertainty_score":0.07839835,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1747328681048122,"score_gpt":0.3873623065599136,"score_spread":0.2126294384551014,"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."}}