{"id":"W4231007923","doi":"10.5194/acp-2020-1119","title":"Where there is smoke there is mercury: Assessing boreal forest firemercury emissions using aircraft and highlighting uncertaintiesassociated with upscaling emissions estimates","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Mercury impact and mitigation studies","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada","funders":"Environment and Climate Change Canada","keywords":"Environmental science; Plume; Mercury (programming language); Atmospheric sciences; Taiga; Environmental chemistry; Biogeochemical cycle; Smoke; Panache; Boreal; Meteorology; Chemistry; Geology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003536696,0.001073495,0.0002934075,0.001194733,0.0005331346,0.002048727,0.001188351,0.0007833018,0.000260923],"category_scores_gemma":[0.004490264,0.0003818188,0.000469659,0.0007662954,0.0002943464,0.001286948,0.0006079847,0.0004471042,0.0001338265],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00169012,"about_ca_system_score_gemma":0.000946194,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1017173,"about_ca_topic_score_gemma":0.1876886,"domain_scores_codex":[0.9989667,0.0002677588,0.00006996052,0.000217644,0.0004044911,0.00007351817],"domain_scores_gemma":[0.9977515,0.0006291863,0.000414086,0.0001827045,0.000933496,0.00008914492],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001213618,0.0001464975,0.9215564,0.0001215542,0.0002102037,0.0001405538,0.0002887931,0.0262669,0.008297258,0.0002425975,0.0003746137,0.04223325],"study_design_scores_gemma":[0.00001807121,0.0004238179,0.7761346,0.0001714016,0.0002151907,0.000198636,0.001289762,0.2041134,0.01370436,0.0009554399,0.002707434,0.00006792801],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9788873,0.00125812,0.01463361,0.0003690037,0.00003917504,0.0001038937,0.0009080918,0.00020514,0.003595651],"genre_scores_gemma":[0.9832786,0.000366524,0.01541695,0.00007565273,0.00001917659,0.00002437764,0.0005702924,0.0000166812,0.000231735],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1017173,"threshold_uncertainty_score":0.2022505,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04956810900954157,"score_gpt":0.3141488434743602,"score_spread":0.2645807344648186,"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."}}