{"id":"W2307397754","doi":"10.5194/amt-9-3491-2016","title":"Bias corrections of GOSAT SWIR XCO<sub>2</sub> and XCH<sub>4</sub> with TCCON data andtheir evaluation using aircraft measurement data","year":2016,"lang":"en","type":"article","venue":"Atmospheric measurement techniques","topic":"Atmospheric and Environmental Gas Dynamics","field":"Environmental Science","cited_by":79,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"National Oceanic and Atmospheric Administration; Ministry of Defense- Japan; Biological and Environmental Research; Universität Bremen; Ministry of Defense; National Aeronautics and Space Administration; U.S. Department of Energy; Canadian Space Agency; Max-Planck-Gesellschaft; National Center for Atmospheric Research; National Science Foundation","keywords":"Environmental science; Greenhouse gas; Satellite; Atmospheric sciences; Meteorology; Physics; Geology","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":[0.001576319,0.0007517636,0.0002142027,0.0006903246,0.0003860073,0.0004606118,0.0005131004,0.000290248,0.000803164],"category_scores_gemma":[0.004504951,0.0002129556,0.0004341965,0.0009291379,0.0001605097,0.0004344884,0.0003963469,0.0002735586,0.0003284922],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000706652,"about_ca_system_score_gemma":0.001114628,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03166071,"about_ca_topic_score_gemma":0.04651177,"domain_scores_codex":[0.9994226,0.0001336322,0.00004565942,0.0001272396,0.0002036905,0.00006724634],"domain_scores_gemma":[0.9987233,0.0002311288,0.0001923474,0.0002364991,0.0005870986,0.00002951139],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006892495,0.0003119401,0.4594045,0.0002272015,0.0006356674,0.0001473462,0.0004891311,0.1518515,0.1152677,0.001724049,0.006709944,0.2625417],"study_design_scores_gemma":[0.0001818502,0.0001354363,0.4208719,0.00005437057,0.0002387211,0.00006780907,0.0001560742,0.4564141,0.1108987,0.0004687445,0.01040873,0.0001035361],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9406415,0.0003719344,0.05036983,0.0001290437,0.0001792907,0.0001143362,0.002112234,0.002430108,0.003651707],"genre_scores_gemma":[0.934994,0.000118006,0.05954934,0.00009763503,0.0000323614,0.00008993436,0.003092793,0.0005376794,0.001488212],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03166071,"threshold_uncertainty_score":0.06295282,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1539767028676742,"score_gpt":0.2868795387206377,"score_spread":0.1329028358529635,"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."}}