{"id":"W4241320369","doi":"10.5194/essd-2020-114","title":"A Decade of GOSAT Proxy Satellite CH <sub>4</sub> Observations","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Atmospheric and Environmental Gas Dynamics","field":"Environmental Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Eurostars; Natural Sciences and Engineering Research Council of Canada; Natural Environment Research Council; Canadian Space Agency; Sorbonne Université; Centre National de la Recherche Scientifique; Korea Meteorological Administration; Centre National d’Etudes Spatiales; Université de La Réunion; Environment and Climate Change Canada; National Aeronautics and Space Administration; Ministry of Environment; University of Leicester; Sight Research UK; National Centre for Earth Observation; Conseil Régional, Île-de-France; European Space Agency","keywords":"Environmental science; Greenhouse gas; Satellite; Proxy (statistics); Meteorology; Atmospheric sciences; Statistics; Geography; Mathematics; Geology; Physics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0008549535,0.0006089279,0.0003584139,0.001305099,0.0003554005,0.0008344647,0.0006611226,0.000493957,0.004329142],"category_scores_gemma":[0.001950013,0.0002711913,0.0004127637,0.003789104,0.0003587621,0.0009653382,0.0009553964,0.0006920574,0.002065544],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001150612,"about_ca_system_score_gemma":0.001006228,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.09183124,"about_ca_topic_score_gemma":0.1256199,"domain_scores_codex":[0.9993353,0.00005177802,0.00004121605,0.0001921918,0.0002981928,0.0000813087],"domain_scores_gemma":[0.998355,0.0002008109,0.0002885718,0.000409835,0.0006301053,0.0001156474],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.001382,0.0001533713,0.2691862,0.00203001,0.000865198,0.0007197817,0.0005960011,0.02373581,0.034798,0.004717734,0.4114063,0.2504095],"study_design_scores_gemma":[0.00007057369,0.00007619245,0.4914685,0.0002366869,0.0001715921,0.000169737,0.000298141,0.006673492,0.01362108,0.0008667106,0.4862378,0.0001094392],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.308903,0.003436952,0.008280891,0.002467489,0.0007296648,0.000152624,0.6540546,0.003558089,0.01841665],"genre_scores_gemma":[0.2254261,0.001665664,0.01653904,0.0009592648,0.0003186694,0.0001817446,0.748513,0.000864705,0.005531961],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.09183124,"threshold_uncertainty_score":0.1825935,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02137636344500854,"score_gpt":0.2197469595740615,"score_spread":0.1983705961290529,"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."}}