{"id":"W2173082680","doi":"10.5194/bg-13-3225-2016","title":"Reconstructions of biomass burning from sediment-charcoal records to improve data–model comparisons","year":2016,"lang":"en","type":"article","venue":"Biogeosciences","topic":"Fire effects on ecosystems","field":"Environmental Science","cited_by":244,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"Office of Integrative Activities; Division of Behavioral and Cognitive Sciences; Division of Emerging Frontiers; Université de Franche-Comté; Centre National de la Recherche Scientifique; National Science Foundation","keywords":"Charcoal; Context (archaeology); Environmental science; Biomass (ecology); Biomass burning; Fire regime; Global change; Climatology; Physical geography; Sediment; Climate change; Ecosystem; Meteorology; Geography; Geology; Oceanography; Ecology; Aerosol; Archaeology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000629634,0.0001527462,0.0002205956,0.00008850942,0.0001850285,0.0000391658,0.001102931,0.00006036538,0.0006431661],"category_scores_gemma":[0.000167619,0.0001027977,0.00004323695,0.0004655833,0.0004830252,0.0006151079,0.000674583,0.00004802464,0.0005113739],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001021113,"about_ca_system_score_gemma":0.00004216041,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02108275,"about_ca_topic_score_gemma":0.002848579,"domain_scores_codex":[0.998041,0.00006872784,0.0003525419,0.0007274435,0.0004481751,0.0003620634],"domain_scores_gemma":[0.9986486,0.0001928123,0.0001978023,0.0007456675,0.00001230189,0.0002027797],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00001074296,0.00005062778,0.3294801,0.000003805999,0.000009649516,8.286457e-7,0.0001210165,0.00001143019,0.6291049,0.00002910389,0.005898081,0.03527973],"study_design_scores_gemma":[0.001648572,0.0009753158,0.3701002,0.0004255445,0.00009617504,0.00002295576,0.0009495783,0.2017718,0.3581582,0.001444426,0.06279973,0.001607443],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9802534,0.00002932946,0.01438348,0.0007231657,0.00134202,0.0002860892,0.001303745,0.00006044113,0.001618332],"genre_scores_gemma":[0.9783091,0.000006488014,0.02124392,0.0000452328,0.00006818661,0.00001834822,0.00001225215,0.000009049912,0.000287455],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2709467,"threshold_uncertainty_score":0.985436,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02790090807370347,"score_gpt":0.2553312677774645,"score_spread":0.227430359703761,"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."}}