{"id":"W4253161388","doi":"10.1002/essoar.10502909.1","title":"Toward High Precision XCO2 Retrievals from TanSat Observations: Retrieval Improvement and Validation against TCCON Measurements","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Atmospheric and Environmental Gas Dynamics","field":"Environmental Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Chinese Academy of Sciences","keywords":"Remote sensing; Calibration; Satellite; Mean squared error; A priori and a posteriori; Nadir; Computer science; Statistics; Mathematics","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":[0.001335527,0.0008395731,0.0004346545,0.001078034,0.0004301854,0.0007479763,0.000706546,0.0005646804,0.0006292912],"category_scores_gemma":[0.002080621,0.0002540522,0.0005088969,0.001365291,0.000320076,0.0007722622,0.0006542135,0.0004491778,0.0003233606],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000569299,"about_ca_system_score_gemma":0.0009426613,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03014551,"about_ca_topic_score_gemma":0.02804427,"domain_scores_codex":[0.9995579,0.00006585788,0.00003001394,0.0001142341,0.0001706771,0.00006126149],"domain_scores_gemma":[0.9994702,0.00009969624,0.00007385396,0.0001115912,0.0002137592,0.00003092306],"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":[0.001017383,0.0006865155,0.1620347,0.0005531476,0.0004278021,0.0005219627,0.0005511427,0.2891198,0.3201834,0.001410123,0.004115112,0.2193789],"study_design_scores_gemma":[0.0002580548,0.0001629081,0.136717,0.00004218437,0.0001517454,0.00007174162,0.0001671822,0.7467266,0.1121268,0.0003196682,0.003176331,0.00007978268],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9463598,0.0002710392,0.04717659,0.0001165463,0.00005472775,0.0001281364,0.001480912,0.001629387,0.002782892],"genre_scores_gemma":[0.9229746,0.0001460614,0.07253615,0.00006646414,0.00002678146,0.00009271852,0.003248658,0.000188401,0.0007201639],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03014551,"threshold_uncertainty_score":0.0599401,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06204359107460061,"score_gpt":0.239412233939782,"score_spread":0.1773686428651814,"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."}}