{"id":"W3023592482","doi":"","title":"Long-term Global Multi-physical Modelling of Ozone Dry Deposition Velocity - with Focus on Process Uncertainty and Implication on Air Quality Modelling","year":2018,"lang":"en","type":"article","venue":"AGU Fall Meeting Abstracts","topic":"Vehicle emissions and performance","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Term (time); Environmental science; Deposition (geology); Focus (optics); Process (computing); Air quality index; Meteorology; Atmospheric sciences; Computer science; Geography; Geology; Physics","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":[0.0002157202,0.0001960228,0.0002085832,0.0000339371,0.0001524724,0.00002307639,0.00009725301,0.0001003009,2.319587e-7],"category_scores_gemma":[0.00001269349,0.0001701617,0.00003130551,0.000133665,0.00006178386,0.0001462543,0.00001387085,0.0002017914,0.000004843796],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008026682,"about_ca_system_score_gemma":0.00002236025,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001342864,"about_ca_topic_score_gemma":0.0003591208,"domain_scores_codex":[0.998911,0.00002130001,0.0003001179,0.0002815404,0.0002325559,0.0002534728],"domain_scores_gemma":[0.999357,0.0000721525,0.000130822,0.0002080281,0.0001251663,0.0001067866],"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":[0.0001219666,0.0001155118,0.0102053,0.0001889938,0.00001901567,0.000001498215,0.0004364924,0.9822406,0.0009387062,0.00003584202,8.026457e-7,0.00569524],"study_design_scores_gemma":[0.0003848852,0.0001660388,0.06125329,0.0005394681,0.00001679853,0.000002937124,0.00003028021,0.9165648,0.02069517,0.0001437133,4.514992e-7,0.0002021894],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9414732,0.00002840699,0.05727392,0.000024297,0.00003373058,0.0001606625,0.00001329539,0.000131273,0.0008611818],"genre_scores_gemma":[0.9947593,0.00001834222,0.005032845,0.0000114622,0.0001272488,0.00001230155,0.00001442371,0.00002194222,0.000002141064],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06567585,"threshold_uncertainty_score":0.6938991,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03065718771694185,"score_gpt":0.2856241382572208,"score_spread":0.2549669505402789,"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."}}