{"id":"W4309030160","doi":"10.1016/j.rse.2022.113335","title":"Paddy rice methane emissions across Monsoon Asia","year":2022,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Atmospheric and Environmental Gas Dynamics","field":"Environmental Science","cited_by":70,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"College of Engineering, Michigan State University; Division of Chemical, Bioengineering, Environmental, and Transport Systems; HORIZON EUROPE Framework Programme; U.S. Geological Survey; Ministry of Agriculture, Forestry and Fisheries; European Commission; Chinese Academy of Agricultural Sciences; Rural Development Administration; Ministry of Environment; National Natural Science Foundation of China; National Research Foundation; Michigan State University; National Research Foundation of Korea; Gordon and Betty Moore Foundation; National Science Foundation","keywords":"Environmental science; Eddy covariance; Paddy field; Monsoon; Greenhouse gas; Methane; Atmospheric sciences; Climatology; Ecosystem; Agronomy; Ecology","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004349175,0.0002701567,0.0002976787,0.000009742574,0.000446581,0.000008727654,0.0003029857,0.00007054566,0.002447371],"category_scores_gemma":[0.00001761924,0.0002826288,0.000155605,0.0001955787,0.0003972515,0.00008234356,0.001320163,0.0003379963,0.0001606094],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008663253,"about_ca_system_score_gemma":0.000007589402,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007696442,"about_ca_topic_score_gemma":0.000005841131,"domain_scores_codex":[0.997489,0.0001682047,0.0004115588,0.000549937,0.0008480178,0.0005333037],"domain_scores_gemma":[0.9988489,0.00006227489,0.0002490696,0.0006400371,0.000001092111,0.0001985947],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007603503,0.0002626791,0.002183871,0.00001211755,0.00005164196,0.00008122143,0.00150272,0.6330012,0.1686981,0.000007208957,0.0005378358,0.1935853],"study_design_scores_gemma":[0.00309671,0.001336711,0.1628603,0.00006045952,0.0003105411,0.0006775601,0.0142275,0.4991744,0.02324398,0.002182511,0.2901508,0.002678521],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9643829,0.0001063444,0.02931718,0.0003633082,0.0001612875,0.0002850717,0.00001566674,0.00004828218,0.005319918],"genre_scores_gemma":[0.8995622,0.0001228639,0.09663235,0.0002080928,0.00002425503,8.396163e-7,0.0000155866,0.00005251,0.003381277],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.289613,"threshold_uncertainty_score":0.9999626,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01007487834065876,"score_gpt":0.2276750739216699,"score_spread":0.2176001955810112,"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."}}