{"id":"W4400055623","doi":"10.5194/gmd-17-4983-2024","title":"Can TROPOMI NO <sub>2</sub> satellite data be used to track the drop in and resurgence of NO <sub> <i>x</i> </sub> emissions in Germany between 2019–2021 using the multi-source plume method (MSPM)?","year":2024,"lang":"en","type":"article","venue":"Geoscientific model development","topic":"Atmospheric chemistry and aerosols","field":"Earth and Planetary Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada","funders":"Umweltbundesamt","keywords":"Satellite; Environmental science; Meteorology; Drop (telecommunication); Atmospheric sciences; Remote sensing; Physics; Geography; Computer science; Astronomy","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":[],"consensus_categories":[],"category_scores_codex":[0.002901342,0.0003157206,0.0003719563,0.00005341338,0.0004122427,0.0002388007,0.0009301337,0.0001240342,0.00006500282],"category_scores_gemma":[0.0001452336,0.0002141657,0.00005658285,0.001067332,0.0002295839,0.0002574073,0.0003094567,0.0004027727,0.00004529023],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004299207,"about_ca_system_score_gemma":0.0006326736,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000954901,"about_ca_topic_score_gemma":0.002786319,"domain_scores_codex":[0.9966594,0.000211763,0.0007873037,0.001057386,0.0006212761,0.0006628689],"domain_scores_gemma":[0.9982806,0.000427869,0.0001363393,0.0008354257,0.00006758752,0.000252175],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000779882,0.0001035771,0.02315317,0.0002478181,0.00007423401,0.00005450847,0.01540857,0.07612035,0.7053295,0.000004261452,0.00196889,0.1774571],"study_design_scores_gemma":[0.0003548577,0.00001483054,0.03221716,0.000415506,0.00003706702,0.00001584849,0.000575583,0.8394586,0.1136983,0.00004122554,0.01260191,0.0005691041],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9764425,0.000583278,0.02069032,0.0005928999,0.0003990875,0.0005011437,0.0006899553,0.00002401968,0.00007679966],"genre_scores_gemma":[0.9754962,0.0001698279,0.02283032,0.0001505873,0.00006695692,0.000009259778,0.0004621736,0.00001642386,0.0007982914],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7633382,"threshold_uncertainty_score":0.8733426,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06328095381143256,"score_gpt":0.2816431116183513,"score_spread":0.2183621578069187,"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."}}