{"id":"W4250806234","doi":"10.5194/bg-2017-34","title":"Modelling past, present and future peatland carbon accumulationacross the pan-Arctic","year":2017,"lang":"en","type":"preprint","venue":"","topic":"Peatlands and Wetlands Ecology","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"NordForsk; Lunds Universitet","keywords":"Peat; Permafrost; Carbon sink; Environmental science; Climate change; Holocene; Physical geography; Carbon cycle; Arctic; Greenhouse gas; Ecosystem; Global warming; Sink (geography); Carbon fibers; Climatology; Ecology; Geography; Geology; Oceanography","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":[0.000401139,0.0006151048,0.0002994935,0.0003375463,0.0004488261,0.0009344839,0.000514505,0.0007734531,0.001009248],"category_scores_gemma":[0.0006125727,0.0003485758,0.0006948996,0.0004893263,0.0003696876,0.000686968,0.0003796644,0.0004479463,0.0001077345],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001525055,"about_ca_system_score_gemma":0.001594987,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1349079,"about_ca_topic_score_gemma":0.1267861,"domain_scores_codex":[0.9999111,0.00002345183,0.000006683466,0.00002821833,0.000009317541,0.00002122531],"domain_scores_gemma":[0.9997562,0.0001025761,0.00003912663,0.00001593092,0.00004827363,0.00003797411],"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":[0.00004396751,0.00002624669,0.01123973,0.00002041273,0.00004288981,0.00005105887,0.0000188828,0.9861118,0.0007462336,0.0002385595,0.00006446056,0.001395915],"study_design_scores_gemma":[0.00001646771,0.00004074367,0.01005867,0.000008343568,0.00003103677,0.00001773142,0.00004924379,0.9887021,0.0004557359,0.0002745082,0.0003351394,0.00001039304],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9945607,0.0001140226,0.003011257,0.00005944628,0.00001289432,0.00001059831,0.00049586,0.00005795297,0.0016773],"genre_scores_gemma":[0.9973743,0.00009506669,0.001686895,0.00001143268,0.000003664156,0.00001662376,0.0003098049,0.0000102067,0.0004919933],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1349079,"threshold_uncertainty_score":0.2682454,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02669701010460829,"score_gpt":0.2693793016407759,"score_spread":0.2426822915361676,"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."}}