{"id":"W4220974539","doi":"10.1029/2021gb007146","title":"Using Machine Learning to Predict Inland Aquatic CO<sub>2</sub> and CH<sub>4</sub> Concentrations and the Effects of Wildfires in the Yukon‐Kuskokwim Delta, Alaska","year":2022,"lang":"en","type":"article","venue":"Global Biogeochemical Cycles","topic":"Climate change and permafrost","field":"Earth and Planetary Sciences","cited_by":41,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"U.S. Fish and Wildlife Service; Nuclear Safety and Security Commission; National Aeronautics and Space Administration; National Science Foundation","keywords":"Tundra; Environmental science; Permafrost; Dissolved organic carbon; Arctic; Aquatic ecosystem; Thermokarst; Total organic carbon; Surface water; Hydrology (agriculture); Ecosystem; Delta; Physical geography; Environmental chemistry; Atmospheric sciences; Ecology; Oceanography; Chemistry; Environmental engineering; Geology; Geography","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009691466,0.000780844,0.0003705231,0.0008815083,0.0003773741,0.0008911751,0.0004173512,0.0006342219,0.0006311839],"category_scores_gemma":[0.001410289,0.0003219619,0.000564956,0.0004787954,0.0002447561,0.0004993701,0.0003429457,0.0004062912,0.0002502998],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001342315,"about_ca_system_score_gemma":0.0008142755,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.07966731,"about_ca_topic_score_gemma":0.06716013,"domain_scores_codex":[0.9998023,0.00005644956,0.00001517165,0.00007368831,0.00002005524,0.0000324375],"domain_scores_gemma":[0.9992537,0.000393871,0.0001062308,0.00003925228,0.0001396893,0.00006719594],"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":[0.000213881,0.0004401273,0.5551412,0.00002316946,0.0002476812,0.000063634,0.00005386803,0.4228716,0.001604425,0.00007728229,0.0005986713,0.01866459],"study_design_scores_gemma":[0.000007865821,0.00004022427,0.05480404,0.000006524957,0.0000249081,0.000008802675,0.00004743556,0.9443727,0.0004208259,0.000148788,0.0001101447,0.000007747402],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9961345,0.00006759396,0.002779065,0.00007977134,0.00001222676,0.00001096954,0.0004097223,0.00009083418,0.0004154388],"genre_scores_gemma":[0.9977027,0.0000239595,0.001490871,0.00001505284,0.000005546601,0.000009699208,0.0004907081,0.000003737025,0.0002577213],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07966731,"threshold_uncertainty_score":0.1584072,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01438288607798476,"score_gpt":0.232971058292181,"score_spread":0.2185881722141963,"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."}}