{"id":"W4389307917","doi":"10.5194/egusphere-2023-2550","title":"Estimation of Canada’s methane emissions: inverse modelling analysis using the ECCC measurement network","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Atmospheric and Environmental Gas Dynamics","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada","funders":"","keywords":"Environmental science; Boreal; Methane emissions; Inversion (geology); Methane; Climate change; Atmospheric sciences; Wetland; Climatology; Taiga; Greenhouse gas; Fossil fuel; Geography; Geology; Ecology; Oceanography","routes":{"ca_aff":true,"ca_fund":false,"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":[0.000674759,0.0008169381,0.0004123995,0.0008209532,0.0009344202,0.0007276336,0.00109336,0.0005060225,0.0008909659],"category_scores_gemma":[0.001358429,0.0003059276,0.001119111,0.001696261,0.0003874146,0.0003679722,0.0005008257,0.001006762,0.0002002346],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005626537,"about_ca_system_score_gemma":0.01321231,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.963907,"about_ca_topic_score_gemma":0.9315669,"domain_scores_codex":[0.9996403,0.00004160322,0.00001245995,0.00007525062,0.0001368446,0.00009345471],"domain_scores_gemma":[0.9994542,0.00008134311,0.000037767,0.00004114849,0.0003480233,0.00003755122],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002086654,0.0001575998,0.189062,0.0001350858,0.0004299605,0.0002228006,0.0001619569,0.7603086,0.00337526,0.001807068,0.008768348,0.03536259],"study_design_scores_gemma":[0.00002926145,0.00001184382,0.07326563,0.00001754493,0.00006696192,0.00001846465,0.0001148115,0.921795,0.001503878,0.000239437,0.002886702,0.00005051515],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9682345,0.0003328068,0.01335882,0.0004684358,0.00004681343,0.00004504108,0.01238642,0.0006560495,0.004471037],"genre_scores_gemma":[0.975709,0.0001261607,0.01004783,0.00005628603,0.00001239671,0.00002783051,0.01291557,0.00005956521,0.001045365],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.036093,"threshold_uncertainty_score":0.07261103,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05498635679840588,"score_gpt":0.2413934707223379,"score_spread":0.186407113923932,"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."}}