{"id":"W2778679701","doi":"10.5194/acp-18-6483-2018","title":"High-resolution inversion of methane emissions in the Southeast US using SEAC <sup>4</sup> RS aircraft observations of atmospheric methane: anthropogenic and wetland sources","year":2018,"lang":"en","type":"article","venue":"Atmospheric chemistry and physics","topic":"Atmospheric and Environmental Gas Dynamics","field":"Environmental Science","cited_by":57,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Earth Sciences Division; National Oceanic and Atmospheric Administration; National Ethnic Affairs Commission of the People's Republic of China; Langley Research Center; National Aeronautics and Space Administration; California Institute of Technology; Jet Propulsion Laboratory","keywords":"Environmental science; Methane; Wetland; Greenhouse gas; Inversion (geology); Atmospheric sciences; Atmospheric methane; Hydrology (agriculture); Climatology; Meteorology; Geology; Chemistry; Oceanography; 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":[0.0002605268,0.0003390278,0.000192676,0.0003019843,0.0002284217,0.000307712,0.0003754513,0.0002485105,0.0008086771],"category_scores_gemma":[0.0005448402,0.000227154,0.0004104322,0.0004777719,0.0001762188,0.0002586993,0.0003058699,0.0003007835,0.0001656217],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006092396,"about_ca_system_score_gemma":0.001418364,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1540978,"about_ca_topic_score_gemma":0.1858201,"domain_scores_codex":[0.9999362,0.00001156269,0.00000407703,0.00001669614,0.00001869305,0.00001276023],"domain_scores_gemma":[0.9998468,0.00002672825,0.00003267439,0.00002009186,0.00005498336,0.00001870771],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0006972918,0.0003836114,0.37349,0.00009973248,0.0003821529,0.0003346618,0.0002078869,0.5193938,0.03890847,0.001056746,0.006468421,0.05857724],"study_design_scores_gemma":[0.0001287299,0.00004745033,0.1611354,0.0000159716,0.00007830014,0.0000311913,0.00009495973,0.8274642,0.008264476,0.0004235745,0.002283243,0.00003260986],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9931152,0.00005073535,0.00305162,0.0001093064,0.00001353678,0.000006189808,0.001874949,0.0003914029,0.001387013],"genre_scores_gemma":[0.9925633,0.00002491635,0.004418895,0.00002619272,0.000006905316,0.000006531883,0.002658245,0.00003175104,0.0002633998],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1540978,"threshold_uncertainty_score":0.3064016,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01218999714061121,"score_gpt":0.2187119732590687,"score_spread":0.2065219761184575,"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."}}