{"id":"W4311681088","doi":"10.22215/etd/2022-15313","title":"Atmospheric Methane Data Assimilation in the CMAQ Air Quality Model","year":2022,"lang":"en","type":"dissertation","venue":"","topic":"Atmospheric and Environmental Gas Dynamics","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"National Oceanic and Atmospheric Administration; Environment and Climate Change Canada; Compute Canada","keywords":"Data assimilation; Methane; Greenhouse gas; Air quality index; Environmental science; CMAQ; Covariance; Atmospheric methane; Meteorology; Parametric statistics; Statistics; Mathematics; Chemistry; Geography","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0006328506,0.0006932377,0.0005335001,0.0002932825,0.0005768872,0.0008844159,0.001310065,0.001121728,0.005943165],"category_scores_gemma":[0.0008147769,0.0004594524,0.0009828086,0.0009297724,0.0001908851,0.0008377128,0.0005108144,0.001459401,0.002455183],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009782074,"about_ca_system_score_gemma":0.002044584,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1583868,"about_ca_topic_score_gemma":0.1099606,"domain_scores_codex":[0.9997557,0.00004008623,0.000010905,0.00008374838,0.00006763812,0.00004184734],"domain_scores_gemma":[0.9997423,0.00002363354,0.00001121412,0.00004026485,0.0001536236,0.00002897122],"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":[0.0003228657,0.0002243207,0.01371288,0.0001574923,0.0002884783,0.0001086218,0.00007501218,0.8707311,0.005222047,0.005536255,0.07273871,0.03088221],"study_design_scores_gemma":[0.0003404265,0.0000536507,0.01145127,0.00004246671,0.00006475596,0.00001866448,0.00005431926,0.9477878,0.00279641,0.002080261,0.03523115,0.00007888323],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4288182,0.002027059,0.09134515,0.005857268,0.003666166,0.0006359332,0.3707386,0.01035915,0.08655247],"genre_scores_gemma":[0.7498019,0.000590764,0.06634421,0.000676022,0.0002145852,0.0004789211,0.1640486,0.0009458463,0.0168991],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1583868,"threshold_uncertainty_score":0.3149298,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03401556656111948,"score_gpt":0.3067509423864807,"score_spread":0.2727353758253612,"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."}}