{"id":"W2288367337","doi":"10.5194/bg-13-1329-2016","title":"Evaluation of wetland methane emissions across North America using atmospheric data and inverse modeling","year":2016,"lang":"en","type":"article","venue":"Biogeosciences","topic":"Atmospheric and Environmental Gas Dynamics","field":"Environmental Science","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada","funders":"National Nuclear Security Administration; Office of Science; National Aeronautics and Space Administration; U.S. Department of Energy; National Oceanic and Atmospheric Administration; Harvard University; National Science Foundation","keywords":"Environmental science; Wetland; Seasonality; Magnitude (astronomy); Atmospheric sciences; Land cover; Spatial distribution; Climatology; Land use; Geography; Geology; Remote sensing; Ecology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008091523,0.0001122243,0.0001241427,0.000002124238,0.0001677626,0.00001419797,0.0004069697,0.00003625481,0.000295154],"category_scores_gemma":[0.0001128282,0.00007075325,0.00001956181,0.0003141261,0.0008341731,0.000491914,0.0006926838,0.0000301298,0.00001226741],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001038053,"about_ca_system_score_gemma":0.00003046897,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002647411,"about_ca_topic_score_gemma":0.0004997205,"domain_scores_codex":[0.9983383,0.00007411555,0.0001911314,0.0004284171,0.0007212227,0.0002468608],"domain_scores_gemma":[0.9993579,0.00003108189,0.0001183683,0.0003667577,0.000009602862,0.0001163318],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000008818771,0.00005591832,0.646841,0.000002992688,0.000010419,8.158594e-7,0.0005603208,0.1085638,0.02700618,9.055232e-7,0.00002375824,0.216925],"study_design_scores_gemma":[0.0002119565,0.00003327265,0.02734307,0.00001215512,0.00004307014,0.000003484375,0.0007644219,0.9708664,0.0000768202,0.0001088648,0.0004140928,0.0001224034],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9559349,0.00008635691,0.04357127,0.00005494318,0.00006770501,0.0001138082,0.00003867891,0.00001100071,0.0001212703],"genre_scores_gemma":[0.9569601,0.0002128611,0.04271299,0.00003381731,0.00001187215,0.00000265115,0.000004724986,0.000006132644,0.00005479934],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8623026,"threshold_uncertainty_score":0.4002111,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05973099645906784,"score_gpt":0.2992454619037092,"score_spread":0.2395144654446413,"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."}}