{"id":"W2169203464","doi":"10.1029/2010jg001390","title":"Ecosystem carbon dioxide fluxes after disturbance in forests of North America","year":2010,"lang":"en","type":"article","venue":"Journal of Geophysical Research Atmospheres","topic":"Fire effects on ecosystems","field":"Environmental Science","cited_by":848,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University; Université Laval; Canadian Forest Service; University of British Columbia; Environment and Climate Change Canada; University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Ecosystem; Eddy covariance; Ecosystem respiration; Environmental science; Taiga; Carbon sink; Boreal; Temperate rainforest; Ecology; Primary production; Disturbance (geology); Forest ecology; Thinning; Chronosequence; Temperate climate; Boreal ecosystem; Carbon cycle; Temperate forest; Forestry; Geography; Biology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000920633,0.0001775944,0.0005095819,0.00003010034,0.0000457046,0.00003145626,0.0007103754,0.00007500435,0.0002404649],"category_scores_gemma":[0.0008053996,0.000132847,0.0001688571,0.0007838635,0.0003830552,0.0003122255,0.0002759077,0.00113562,0.0001201256],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002042663,"about_ca_system_score_gemma":0.00007025658,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005032291,"about_ca_topic_score_gemma":0.04487815,"domain_scores_codex":[0.9965395,0.0003744945,0.0006478518,0.0002698378,0.001565814,0.0006025488],"domain_scores_gemma":[0.9981011,0.0007588738,0.0003227456,0.000415356,0.0001070719,0.0002949068],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003660478,0.0004067385,0.9626375,0.00008037317,0.00002458861,0.0002204763,0.0002428791,0.0002189204,0.01982013,0.000009282664,0.0004205949,0.0155525],"study_design_scores_gemma":[0.0003910514,0.0006595829,0.9903489,0.0001253998,0.000005764459,0.0000140668,0.00003924999,0.003102382,0.002554446,0.0002433297,0.002386602,0.0001292091],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9983773,0.00008594878,0.0000115843,0.0001156287,0.0002176385,0.0002721221,0.000008023389,0.000006480233,0.0009052233],"genre_scores_gemma":[0.999064,0.00001530275,0.0004252205,0.00001026459,0.0002394361,0.00002540184,5.427614e-7,0.00002508784,0.0001946911],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03984586,"threshold_uncertainty_score":0.9725503,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007625262651223032,"score_gpt":0.257611527845872,"score_spread":0.2499862651946489,"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."}}