{"id":"W2104279177","doi":"10.5194/bg-10-8233-2013","title":"Simulating boreal forest carbon dynamics after stand-replacing fire disturbance: insights from a global process-based vegetation model","year":2013,"lang":"en","type":"article","venue":"Biogeosciences","topic":"Fire effects on ecosystems","field":"Environmental Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Lawrence Berkeley National Laboratory; Canadian Foundation for Climate and Atmospheric Sciences; Oak Ridge National Laboratory; Biological and Environmental Research; Natural Sciences and Engineering Research Council of Canada; Centre National de la Recherche Scientifique; Natural Resources Canada; Université Laval; National Science Foundation; European Space Agency; Microsoft Research; U.S. Department of Energy","keywords":"Chronosequence; Environmental science; Carbon sink; Basal area; Taiga; Primary production; Coarse woody debris; Boreal; Atmospheric sciences; Vegetation (pathology); Carbon sequestration; Forest ecology; Carbon cycle; Ecosystem; Forestry; Fire regime; Ecology; Soil science; Carbon dioxide; Geography; Geology; Soil water","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001356355,0.0002262473,0.0001894506,0.00002624772,0.000205628,0.000183786,0.0003440173,0.00009488728,0.0000217193],"category_scores_gemma":[0.00008286786,0.0001787192,0.00005172984,0.0005333898,0.0002770035,0.0007035637,0.00008199328,0.00008104963,0.0000509718],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005223576,"about_ca_system_score_gemma":0.00006111691,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.0407401,"about_ca_topic_score_gemma":0.01943416,"domain_scores_codex":[0.9979587,0.00005295919,0.0002944395,0.0006187852,0.0006881263,0.0003869681],"domain_scores_gemma":[0.9992999,0.00008719936,0.0001779038,0.0002650146,0.00002513247,0.0001448762],"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":[0.00001645594,0.00004484735,0.9490041,0.00003106946,0.00000311343,0.000003643571,0.0004095575,0.04654654,0.0002452639,0.00001700201,0.00001128011,0.00366718],"study_design_scores_gemma":[0.0001453751,0.0000443159,0.3827688,0.00005801701,0.00000597243,3.597519e-7,0.00006495461,0.6154777,0.00007916325,0.001195075,0.000004551103,0.0001557109],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9940281,0.0001670333,0.001975368,0.00007290455,0.0002213643,0.0005091552,0.00004481897,0.00009660099,0.002884655],"genre_scores_gemma":[0.9986265,0.000001191099,0.001049654,0.00009189478,0.00004268269,0.0001238702,0.0000279446,0.00001178885,0.00002447871],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5689311,"threshold_uncertainty_score":0.9984586,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005224488242432574,"score_gpt":0.2149136088471047,"score_spread":0.2096891206046721,"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."}}