{"id":"W4390748167","doi":"10.1016/j.isci.2024.108856","title":"An optimized water table depth detected for mitigating global warming potential of greenhouse gas emissions in wetland of Qinghai-Tibetan Plateau","year":2024,"lang":"en","type":"article","venue":"iScience","topic":"Peatlands and Wetlands Ecology","field":"Environmental Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Greenhouse gas; Wetland; Water table; Plateau (mathematics); Environmental science; Global warming; Table (database); Climate change; Global-warming potential; Hydrology (agriculture); Environmental protection; Atmospheric sciences; Ecology; Geology; Groundwater; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001179405,0.0001155914,0.0001121566,0.000170894,0.0002196748,0.000255528,0.000117526,0.0001394122,0.0003308418],"category_scores_gemma":[0.0001133518,0.000057323,0.0001128811,0.0001715599,0.0001298731,0.0001828325,0.0001120803,0.00008826511,0.00002157657],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002281005,"about_ca_system_score_gemma":0.0002785733,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01084493,"about_ca_topic_score_gemma":0.02123824,"domain_scores_codex":[0.9999679,0.000004827296,0.000001453406,0.000008888398,0.000004953658,0.00001186993],"domain_scores_gemma":[0.9999524,0.000006848373,0.00001077089,0.000002289173,0.00001511174,0.00001255078],"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":[0.0003545444,0.00009486033,0.3077735,0.0001007836,0.00004600702,0.0003172919,0.0003312647,0.007736544,0.6661478,0.0003061783,0.0001812073,0.01661006],"study_design_scores_gemma":[0.00001466315,0.0001890837,0.9615691,0.000004738025,0.00002739436,0.00005550907,0.0005126773,0.01385827,0.02300328,0.0001556247,0.000595703,0.00001376293],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.999373,0.00002195226,0.0003839836,0.000007073802,9.969491e-7,0.00000170313,0.00003402829,0.000004691926,0.000172618],"genre_scores_gemma":[0.9996613,0.000009211378,0.0002425134,0.000003261131,5.603869e-7,0.000001751325,0.00003293129,8.633731e-7,0.00004760403],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01084493,"threshold_uncertainty_score":0.02156359,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009500355247150405,"score_gpt":0.2525200583521719,"score_spread":0.2430197031050216,"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."}}