{"id":"W2269985179","doi":"10.1002/2015jd024157","title":"Combining GOSAT <i>X</i>CO<sub>2</sub> observations over land and ocean to improve regional CO<sub>2</sub> flux estimates","year":2016,"lang":"en","type":"article","venue":"Journal of Geophysical Research Atmospheres","topic":"Atmospheric and Environmental Gas Dynamics","field":"Environmental Science","cited_by":84,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Jet Propulsion Laboratory; Canadian Space Agency; National Oceanic and Atmospheric Administration; Environment and Climate Change Canada; National Aeronautics and Space Administration","keywords":"Environmental science; Satellite; Sink (geography); Climatology; Carbon sink; Greenhouse gas; Flux (metallurgy); Atmospheric sciences; Data assimilation; Carbon flux; Geography; Meteorology; Climate change; Oceanography; Geology; Chemistry","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007966633,0.0003439469,0.0004713259,0.00001289672,0.0004526305,0.0001374564,0.0005543614,0.0001498341,0.0001710871],"category_scores_gemma":[0.0004718639,0.0002419736,0.0001874165,0.0004024417,0.001063823,0.0008045789,0.0005073667,0.0006392925,0.0003150429],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005066721,"about_ca_system_score_gemma":0.00007891254,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001684914,"about_ca_topic_score_gemma":0.00004590798,"domain_scores_codex":[0.9960538,0.0001831384,0.0005928946,0.000528215,0.001716183,0.0009257928],"domain_scores_gemma":[0.9972928,0.001151351,0.0002615899,0.0003717182,0.00006927733,0.000853272],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0003355683,0.0003588411,0.1709798,0.00001799804,0.00008217747,0.00009430273,0.0001807141,0.001275223,0.7728919,0.0001253275,0.0151232,0.03853495],"study_design_scores_gemma":[0.00222884,0.002007962,0.8905666,0.0002562899,0.00005299418,0.0000883416,0.0002719529,0.003493509,0.08778844,0.007466314,0.005152557,0.0006262388],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9961104,0.00007967103,0.001531895,0.001538596,0.00009357774,0.0002937813,0.00001643957,0.00002487455,0.0003108071],"genre_scores_gemma":[0.993687,0.000444952,0.004815186,0.000382117,0.0002575553,0.00001195749,0.000004783534,0.00006006868,0.0003363235],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7195867,"threshold_uncertainty_score":0.9867396,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02227295094697284,"score_gpt":0.2780814651664024,"score_spread":0.2558085142194296,"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."}}