{"id":"W3193333906","doi":"10.1002/essoar.10507761.1","title":"Deep learning to evaluate US NOx emissions using surface ozone predictions","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Atmospheric and Environmental Gas Dynamics","field":"Environmental Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"National Eye Institute; Natural Sciences and Engineering Research Council of Canada; Jet Propulsion Laboratory; Canadian Space Agency; Compute Canada; California Institute of Technology; National Aeronautics and Space Administration","keywords":"NOx; Ozone; Ozone Monitoring Instrument; Environmental science; Emission inventory; Atmospheric sciences; Satellite; Nitrogen oxides; Air quality index; Meteorology; Climatology; Geography; Chemistry; Geology; Combustion","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":[0.001204881,0.001335306,0.0005026179,0.0006533699,0.0002607701,0.0006351586,0.000728447,0.0009274486,0.001315429],"category_scores_gemma":[0.002705356,0.0003324588,0.0005925784,0.0004669493,0.0002906124,0.000786192,0.0007083042,0.001095636,0.0002854937],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001006132,"about_ca_system_score_gemma":0.001154865,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02594551,"about_ca_topic_score_gemma":0.02010621,"domain_scores_codex":[0.9997508,0.00005717394,0.00001843953,0.00007935309,0.00003732693,0.00005687776],"domain_scores_gemma":[0.9992823,0.0003624581,0.00007699543,0.00004376111,0.0001714168,0.00006309544],"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":[0.000348452,0.0006094377,0.05629537,0.0001130133,0.0002806593,0.0001083238,0.00003076103,0.8705878,0.001612071,0.0006573349,0.004363009,0.06499376],"study_design_scores_gemma":[0.00001048218,0.00004330726,0.001573725,0.000005530297,0.00001011211,0.000003775146,0.000008396712,0.9973556,0.000491589,0.000367863,0.0001266268,0.000003079697],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9505789,0.001267188,0.04012027,0.0009568294,0.0001702569,0.00008183942,0.002102707,0.001602112,0.003119839],"genre_scores_gemma":[0.9887823,0.0001669728,0.007174688,0.0001880843,0.0000305221,0.00003930899,0.002641164,0.00002528214,0.0009516754],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02594551,"threshold_uncertainty_score":0.05158901,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01729320536842528,"score_gpt":0.2565311491469972,"score_spread":0.2392379437785719,"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."}}