{"id":"W4281491556","doi":"10.3390/atmos13060854","title":"Temporal and Spatial Variation of Wetland CH4 Emissions from the Qinghai–Tibet Plateau under Future Climate Change Scenarios","year":2022,"lang":"en","type":"article","venue":"Atmosphere","topic":"Atmospheric and Environmental Gas Dynamics","field":"Environmental Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"National Natural Science Foundation of China","keywords":"Wetland; Plateau (mathematics); Environmental science; Climate change; Greenhouse gas; Representative Concentration Pathways; Spatial distribution; Climatology; Global warming; Methane; Physical geography; Climate model; Atmospheric sciences; Ecology; Geology; Geography; Remote sensing; Oceanography","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001529459,0.0001652236,0.0001591091,5.276284e-7,0.0005086689,0.00001792489,0.0002323076,0.00007218236,0.004006866],"category_scores_gemma":[0.00000393767,0.0001247677,0.00004934178,0.0001297014,0.0001478894,0.000131868,0.0006515571,0.0002296712,0.00001406117],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001453849,"about_ca_system_score_gemma":0.000006714206,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00935249,"about_ca_topic_score_gemma":0.0008279516,"domain_scores_codex":[0.9988156,0.00008719502,0.0002053434,0.0003045413,0.0003410236,0.0002462722],"domain_scores_gemma":[0.9994399,0.00004946814,0.0001567573,0.0002688396,0.000001826851,0.00008323308],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001063813,0.000164747,0.9007071,0.00000796656,0.00004329624,0.00001024882,0.004377929,0.06433308,0.001102198,0.0001693966,0.000741753,0.02823595],"study_design_scores_gemma":[0.0005916111,0.0001219922,0.9242318,0.000009826945,0.00005002653,0.000009459663,0.003789132,0.05864863,0.0000108624,0.0006708034,0.01164023,0.0002256486],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9951446,0.0002360216,0.001810329,0.001475801,0.0002703088,0.0003040233,0.00005998161,0.00002962429,0.0006693297],"genre_scores_gemma":[0.9957126,0.0001618287,0.003062246,0.0005984195,0.000155528,0.00003792997,0.00008242884,0.0000242856,0.000164777],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0280103,"threshold_uncertainty_score":0.9972443,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008251019540568286,"score_gpt":0.1963216147110039,"score_spread":0.1880705951704356,"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."}}