{"id":"W4296838085","doi":"10.5194/acp-22-12493-2022","title":"Reconciling the total carbon budget for boreal forest wildfire emissions using airborne observations","year":2022,"lang":"en","type":"article","venue":"Atmospheric chemistry and physics","topic":"Atmospheric chemistry and aerosols","field":"Earth and Planetary Sciences","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Forest Service; National Research Council Canada; Natural Resources Canada; University of Waterloo; Environment and Climate Change Canada","funders":"","keywords":"Environmental science; Taiga; Boreal; Air quality index; Atmospheric sciences; Biome; Climate change; Emission inventory; Climatology; Meteorology; Ecosystem; Ecology; Geography; Forestry; Oceanography; Geology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001099922,0.0009397281,0.0003792931,0.0008065485,0.0004440831,0.0008050849,0.0005333527,0.0005895045,0.0006130785],"category_scores_gemma":[0.000765258,0.0003101655,0.0007547641,0.0009648343,0.0001824764,0.001285049,0.0003209065,0.0003262628,0.0001327416],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008336957,"about_ca_system_score_gemma":0.0007745635,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05037365,"about_ca_topic_score_gemma":0.06034357,"domain_scores_codex":[0.9997177,0.00005276387,0.00001984303,0.0001123609,0.00005896185,0.00003826544],"domain_scores_gemma":[0.999744,0.00007032667,0.00003805384,0.00003513882,0.00009358642,0.00001897078],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004607461,0.0002374341,0.7293325,0.0002688697,0.0009697769,0.0003643213,0.0001429626,0.1441933,0.04155065,0.001545473,0.001692641,0.07924146],"study_design_scores_gemma":[0.00006785348,0.0001584782,0.5565159,0.00006525331,0.0003713707,0.0001556999,0.0002916202,0.424253,0.01212563,0.001525857,0.004392564,0.00007685566],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.982361,0.001458658,0.01217019,0.000155449,0.00007449413,0.00002964203,0.001923759,0.000194312,0.001632541],"genre_scores_gemma":[0.9931266,0.0002520634,0.004864529,0.00003976685,0.00001921259,0.00001464875,0.001546706,0.00003290175,0.00010352],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05037365,"threshold_uncertainty_score":0.1001609,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02145243379840814,"score_gpt":0.2176064519482462,"score_spread":0.1961540181498381,"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."}}